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Data Mining:
Concepts and Techniques
(3rd ed.)
— Chapter 1: Introduction —
Jiawei Han, Micheline Kamber, and Jian Pei
University of Illinois at Urbana-Champaign &
Simon Fraser University
©2010 Han, Kamber & Pei. All rights reserved.
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CPSC 5306 Research Topics in Data
management
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Research Topic for 2014: Data Mining
Instructor: Kalpdrum Passi
 Office: FA 380
 Home page: www.cs.laurentian.ca/kpassi/cpsc5306.html
 Teaching: Mon/Wed 4:00-5:30 pm (FA 358)
 Office hours: Mon/Wed 11:30 – 12.30 pm (FA 380)
Prerequisites
 General background: Knowledge on statistics, machine
learning, and data and information systems will help
understand the course materials
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Textbook & Recommended Reference
Books
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Textbook:
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Recommended reference books
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Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 3nd ed.,
Morgan Kaufmann, 2011
C. M. Bishop, Pattern Recognition and Machine Learning, Springer 2007.
S. Chakrabarti, "Mining the Web: Statistical Analysis of Hypertext and Semi-Structured
Data", Morgan Kaufmann, 2002
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2001.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data
Mining, Inference, and Prediction,2nd ed., Springer-Verlag, 2009.
B. Liu, Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer,
2006
C. D. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval,
Cambridge Univ. Press, 2008.
P.-N.Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison-Wesley,
2006 (good preparation materials).
I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and
Techniques with Java Implementations, Morgan Kaufmann, 2nd ed., 2005
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Reference Papers
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Major conference proceedings that will be used
 DM conferences: ACM SIGKDD (KDD), ICDM (IEEE, Int.
Conf. Data Mining), SDM (SIAM Data Mining), PKDD
(Principles KDD)/ECML, PAKDD (Pacific-Asia)
 DB conferences: ACM SIGMOD, VLDB, ICDE
 ML conferences: NIPS, ICML
 IR conferences: SIGIR, CIKM
 Web conferences: WWW, WSDM
Other related conferences and journals
 IEEE TKDE, ACM TKDD, DMKD, ML,
Use course Web page, DBLP, Google Scholar, Citeseer
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Course Evaluation
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Theme-based survey paper: 10%
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Midterm: 30%
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Final Exam: 30%
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Final course project: 30%
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Groups of 2 students
due at the end of semester, but a one-page proposal will be due
at the end of the 3rd week,
evaluation based on (i) technical innovation, (ii) thoroughness of
the work, and (iii) clarity of presentation.
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Chapter 1. Introduction

Why Data Mining?
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What Is Data Mining?
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A Multi-Dimensional View of Data Mining
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What Kind of Data Can Be Mined?
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What Kinds of Patterns Can Be Mined?
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What Technology Are Used?
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What Kind of Applications Are Targeted?
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Major Issues in Data Mining
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A Brief History of Data Mining and Data Mining Society
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Summary
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Why Data Mining?
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The Explosive Growth of Data: from terabytes to petabytes
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Data collection and data availability
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Automated data collection tools, database systems, Web,
computerized society
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Major sources of abundant data
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Business: Web, e-commerce, transactions, stocks, …
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Science: Remote sensing, bioinformatics, scientific simulation, …
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Society and everyone: news, digital cameras, YouTube
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We are drowning in data, but starving for knowledge!
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“Necessity is the mother of invention”—Data mining—Automated
analysis of massive data sets
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Evolution of Sciences
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Before 1600, empirical science
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1600-1950s, theoretical science
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1950s-1990s, computational science
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Over the last 50 years, most disciplines have grown a third, computational branch
(e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.)
Computational Science traditionally meant simulation. It grew out of our inability to
find closed-form solutions for complex mathematical models.
1990-now, data science
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The flood of data from new scientific instruments and simulations
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The ability to economically store and manage petabytes of data online
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The Internet and computing Grid that makes all these archives universally accessible
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Each discipline has grown a theoretical component. Theoretical models often
motivate experiments and generalize our understanding.
Scientific info. management, acquisition, organization, query, and visualization tasks
scale almost linearly with data volumes. Data mining is a major new challenge!
Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science,
Comm. ACM, 45(11): 50-54, Nov. 2002
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Evolution of Database Technology
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1960s:
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1970s:
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Relational data model, relational DBMS implementation
1980s:
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RDBMS, advanced data models (extended-relational, OO, deductive, etc.)
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Application-oriented DBMS (spatial, scientific, engineering, etc.)
1990s:
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Data collection, database creation, IMS and network DBMS
Data mining, data warehousing, multimedia databases, and Web
databases
2000s
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Stream data management and mining
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Data mining and its applications
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Web technology (XML, data integration) and global information systems
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10
Chapter 1. Introduction

Why Data Mining?

What Is Data Mining?

A Multi-Dimensional View of Data Mining

What Kind of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Technology Are Used?

What Kind of Applications Are Targeted?

Major Issues in Data Mining
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A Brief History of Data Mining and Data Mining Society
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Summary
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What Is Data Mining?
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Data mining (knowledge discovery from data)
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Extraction of interesting (non-trivial, implicit, previously
unknown and potentially useful) patterns or knowledge from
huge amount of data
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Alternative names
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Data mining: a misnomer?
Knowledge discovery (mining) in databases (KDD), knowledge
extraction, data/pattern analysis, data archeology, data
dredging, information harvesting, business intelligence, etc.
Watch out: Is everything “data mining”?
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Simple search and query processing
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(Deductive) expert systems
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Knowledge Discovery (KDD) Process
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This is a view from typical
database systems and data
Pattern Evaluation
warehousing communities
Data mining plays an essential
role in the knowledge discovery
Data Mining
process
Task-relevant Data
Data Warehouse
Selection
Data Cleaning
Data Integration
Databases
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Example: A Web Mining Framework
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Web mining usually involves
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Data cleaning
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Data integration from multiple sources
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Warehousing the data
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Data cube construction
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Data selection for data mining
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Data mining
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Presentation of the mining results
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Patterns and knowledge to be used or stored into
knowledge-base
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Data Mining in Business Intelligence
Increasing potential
to support
business decisions
Decision
Making
Data Presentation
Visualization Techniques
End User
Business
Analyst
Data Mining
Information Discovery
Data
Analyst
Data Exploration
Statistical Summary, Querying, and Reporting
Data Preprocessing/Integration, Data Warehouses
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems
DBA
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Example: Mining vs. Data Exploration
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Business intelligence view
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Warehouse, data cube, reporting but not much mining
Business objects vs. data mining tools
Supply chain example: tools
Data presentation
Exploration
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KDD Process: A Typical View from ML and
Statistics
Input Data
Data PreProcessing
Data integration
Normalization
Feature selection
Dimension reduction
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Data
Mining
Pattern discovery
Association & correlation
Classification
Clustering
Outlier analysis
…………
PostProcessing
Pattern
Pattern
Pattern
Pattern
evaluation
selection
interpretation
visualization
This is a view from typical machine learning and statistics communities
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Example: Medical Data Mining
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Health care & medical data mining – often
adopted such a view in statistics and machine
learning
Preprocessing of the data (including feature
extraction and dimension reduction)
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Classification or/and clustering processes
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Post-processing for presentation
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Chapter 1. Introduction

Why Data Mining?

What Is Data Mining?

A Multi-Dimensional View of Data Mining

What Kind of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Technology Are Used?

What Kind of Applications Are Targeted?

Major Issues in Data Mining
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A Brief History of Data Mining and Data Mining Society
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Summary
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Multi-Dimensional View of Data Mining
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Data to be mined
 Database data (extended-relational, object-oriented, heterogeneous,
legacy), data warehouse, transactional data, stream, spatiotemporal,
time-series, sequence, text and web, multi-media, graphs & social
and information networks
Knowledge to be mined (or: Data mining functions)
 Characterization, discrimination, association, classification,
clustering, trend/deviation, outlier analysis, etc.
 Descriptive vs. predictive data mining
 Multiple/integrated functions and mining at multiple levels
Techniques utilized
 Data-intensive, data warehouse (OLAP), machine learning, statistics,
pattern recognition, visualization, high-performance, etc.
Applications adapted
 Retail, telecommunication, banking, fraud analysis, bio-data mining,
stock market analysis, text mining, Web mining, etc.
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Chapter 1. Introduction

Why Data Mining?

What Is Data Mining?

A Multi-Dimensional View of Data Mining

What Kind of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Technology Are Used?

What Kind of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society
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Summary
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Data Mining: On What Kinds of Data?
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Database-oriented data sets and applications
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Relational database, data warehouse, transactional database
Advanced data sets and advanced applications
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Data streams and sensor data
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Time-series data, temporal data, sequence data (incl. bio-sequences)
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Structure data, graphs, social networks and multi-linked data
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Object-relational databases
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Heterogeneous databases and legacy databases
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Spatial data and spatiotemporal data
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Multimedia database
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Text databases
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The World-Wide Web
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Chapter 1. Introduction

Why Data Mining?

What Is Data Mining?

A Multi-Dimensional View of Data Mining

What Kind of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Technology Are Used?

What Kind of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society

Summary
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Data Mining Function: (1) Generalization
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Information integration and data warehouse construction
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Data cube technology
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Data cleaning, transformation, integration, and
multidimensional data model
Scalable methods for computing (i.e., materializing)
multidimensional aggregates
OLAP (online analytical processing)
Multidimensional concept description: Characterization
and discrimination
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Generalize, summarize, and contrast data
characteristics, e.g., dry vs. wet region
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Data Mining Function: (2) Association and
Correlation Analysis
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Frequent patterns (or frequent itemsets)
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What items are frequently purchased together in your
Walmart?
Association, correlation vs. causality
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A typical association rule
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Diaper  Beer [0.5%, 75%] (support, confidence)
Are strongly associated items also strongly correlated?
How to mine such patterns and rules efficiently in large
datasets?
How to use such patterns for classification, clustering,
and other applications?
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Data Mining Function: (3) Classification
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Classification and label prediction
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Construct models (functions) based on some training examples
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Describe and distinguish classes or concepts for future prediction
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Predict some unknown class labels
Typical methods
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E.g., classify countries based on (climate), or classify cars
based on (gas mileage)
Decision trees, naïve Bayesian classification, support vector
machines, neural networks, rule-based classification, patternbased classification, logistic regression, k-nearest neighbor, …
Typical applications:
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Credit card fraud detection, direct marketing, classifying stars,
diseases, web-pages, …
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Data Mining Function: (3) Regression
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Classification predicts categorical (discrete, unordered)
labels
Regression models continuous-valued functions
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Classification and regression may be preceded by
relevance analysis
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Identify attributes that are relevant
Example: Classify based on three types of responses to
sales campaign – good, mild, none
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Predict missing or unavailable numerical data values
Prediction refers to both numeric and class labels
Regression analysis is a statistical methodology that is most often used for
numeric prediction
Derive a model based on descriptive features of items – price,
brand, place_made, type, category
Predict the amount of revenue that each item will
generate during upcoming sales based on previous sales
data – regression analysis
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Data Mining Function: (4) Cluster Analysis
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Unsupervised learning (i.e., Class label is unknown)
Group data to form new categories (i.e., clusters), e.g.,
cluster houses to find distribution patterns
Principle: Maximizing intra-class similarity & minimizing
interclass similarity
Many methods and applications
Example: Cluster analysis on customer data to identify
homogeneous subpopulations of customers
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Data Mining Function: (5) Outlier Analysis
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Outlier analysis
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Outlier: A data object that does not comply with the general
behavior of the data
Noise or exception? ― One person’s garbage could be another
person’s treasure
Methods: by product of clustering or regression analysis, …
Detected using statistical tests that assume a distribution or
probability model for the data, or using distance measures where
objects that are remote from any other cluster
Deviation-based methods identify outliers by examining differences
in the main characteristics of objects in a group
Useful in fraud detection, rare events analysis
Example: Uncover fraudulent usage of credit cards by detecting
purchases of unusually large amounts for a given accoutn number in
comparison to regular charges
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Time and Ordering: Sequential Pattern,
Trend and Evolution Analysis
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Sequence, trend and evolution analysis
 Trend, time-series, and deviation analysis: e.g.,
regression and value prediction
 Sequential pattern mining
 e.g., first buy digital camera, then buy large SD
memory cards
 Periodicity analysis
 Motifs and biological sequence analysis
 Approximate and consecutive motifs
 Similarity-based analysis
Mining data streams
 Ordered, time-varying, potentially infinite, data streams
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Structure and Network Analysis
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Graph mining
 Finding frequent subgraphs (e.g., chemical compounds), trees
(XML), substructures (web fragments)
Information network analysis
 Social networks: actors (objects, nodes) and relationships (edges)
 e.g., author networks in CS, terrorist networks
 Multiple heterogeneous networks
 A person could be multiple information networks: friends,
family, classmates, …
 Links carry a lot of semantic information: Link mining
Web mining
 Web is a big information network: from PageRank to Google
 Analysis of Web information networks
 Web community discovery, opinion mining, usage mining, …
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Evaluation of Knowledge
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Are all mined knowledge interesting?
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One can mine tremendous amount of “patterns” and knowledge
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Some may fit only certain dimension space (time, location, …)
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Some may not be representative, may be transient, …
Evaluation of mined knowledge → directly mine only
interesting knowledge?
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Descriptive vs. predictive
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Coverage
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Typicality vs. novelty
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Accuracy
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Timeliness
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…
32
Chapter 1. Introduction

Why Data Mining?

What Is Data Mining?

A Multi-Dimensional View of Data Mining

What Kind of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Technology Are Used?

What Kind of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society

Summary
33
Data Mining: Confluence of Multiple Disciplines
Machine
Learning
Applications
Algorithm
Pattern
Recognition
Data Mining
Database
Technology
Statistics
Visualization
High-Performance
Computing
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Why Confluence of Multiple Disciplines?
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Tremendous amount of data
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High-dimensionality of data
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Micro-array may have tens of thousands of dimensions
High complexity of data
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Algorithms must be highly scalable to handle such as tera-bytes of
data
Data streams and sensor data
Time-series data, temporal data, sequence data
Structure data, graphs, social networks and multi-linked data
Heterogeneous databases and legacy databases
Spatial, spatiotemporal, multimedia, text and Web data
Software programs, scientific simulations
New and sophisticated applications
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Chapter 1. Introduction

Why Data Mining?

What Is Data Mining?

A Multi-Dimensional View of Data Mining

What Kind of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Technology Are Used?

What Kind of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society

Summary
36
Applications of Data Mining
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Web page analysis: from web page classification, clustering to
PageRank & HITS algorithms
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Collaborative analysis & recommender systems
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Basket data analysis to targeted marketing
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Biological and medical data analysis: classification, cluster analysis
(microarray data analysis), biological sequence analysis, biological
network analysis
Data mining and software engineering (e.g., IEEE Computer, Aug.
2009 issue)
From major dedicated data mining systems/tools (e.g., SAS, MS SQLServer Analysis Manager, Oracle Data Mining Tools) to invisible data
mining
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Chapter 1. Introduction

Why Data Mining?

What Is Data Mining?

A Multi-Dimensional View of Data Mining

What Kind of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Technology Are Used?

What Kind of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society

Summary
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Major Issues in Data Mining (1)
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Mining Methodology
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Mining various and new kinds of knowledge
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Mining knowledge in multi-dimensional space
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Data mining: An interdisciplinary effort
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Boosting the power of discovery in a networked environment
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Handling noise, uncertainty, and incompleteness of data
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Pattern evaluation and pattern- or constraint-guided mining
User Interaction
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Interactive mining
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Incorporation of background knowledge
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Presentation and visualization of data mining results
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Major Issues in Data Mining (2)
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Efficiency and Scalability
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Efficiency and scalability of data mining algorithms
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Parallel, distributed, stream, and incremental mining methods
Diversity of data types
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Handling complex types of data
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Mining dynamic, networked, and global data repositories
Data mining and society
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Social impacts of data mining
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Privacy-preserving data mining
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Invisible data mining
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Chapter 1. Introduction

Why Data Mining?

What Is Data Mining?

A Multi-Dimensional View of Data Mining

What Kind of Data Can Be Mined?

What Kinds of Patterns Can Be Mined?

What Technology Are Used?

What Kind of Applications Are Targeted?

Major Issues in Data Mining

A Brief History of Data Mining and Data Mining Society

Summary
41
A Brief History of Data Mining Society
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1989 IJCAI Workshop on Knowledge Discovery in Databases
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1991-1994 Workshops on Knowledge Discovery in Databases
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Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley,
1991)
Advances in Knowledge Discovery and Data Mining (U. Fayyad, G.
Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996)
1995-1998 International Conferences on Knowledge Discovery in Databases
and Data Mining (KDD’95-98)
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Journal of Data Mining and Knowledge Discovery (1997)
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ACM SIGKDD conferences since 1998 and SIGKDD Explorations

More conferences on data mining

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PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM
(2001), etc.
ACM Transactions on KDD starting in 2007
42
Conferences and Journals on Data Mining

KDD Conferences

ACM SIGKDD Int. Conf. on
Knowledge Discovery in
Databases and Data Mining (KDD)

SIAM Data Mining Conf. (SDM)
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(IEEE) Int. Conf. on Data Mining
(ICDM)

European Conf. on Machine
Learning and Principles and
practices of Knowledge Discovery
and Data Mining (ECML-PKDD)

Pacific-Asia Conf. on Knowledge
Discovery and Data Mining
(PAKDD)

Int. Conf. on Web Search and
Data Mining (WSDM)

Other related conferences



DB conferences: ACM SIGMOD,
VLDB, ICDE, EDBT, ICDT, …
Web and IR conferences: WWW,
SIGIR, WSDM

ML conferences: ICML, NIPS

PR conferences: CVPR,
Journals


Data Mining and Knowledge
Discovery (DAMI or DMKD)
IEEE Trans. On Knowledge and
Data Eng. (TKDE)

KDD Explorations

ACM Trans. on KDD
43
Where to Find References? DBLP, CiteSeer, Google

Data mining and KDD (SIGKDD: CDROM)
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

Database systems (SIGMOD: ACM SIGMOD Anthology—CD ROM)

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Conferences: SIGIR, WWW, CIKM, etc.
Journals: WWW: Internet and Web Information Systems,
Statistics
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Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc.
Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems,
IEEE-PAMI, etc.
Web and IR


Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA
Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc.
AI & Machine Learning


Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.
Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD
Conferences: Joint Stat. Meeting, etc.
Journals: Annals of statistics, etc.
Visualization


Conference proceedings: CHI, ACM-SIGGraph, etc.
Journals: IEEE Trans. visualization and computer graphics, etc.
44
Recommended Reference Books

S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan
Kaufmann, 2002

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000

T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003

U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and
Data Mining. AAAI/MIT Press, 1996

U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge
Discovery, Morgan Kaufmann, 2001

J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd ed., 2006 (3ed.
2011)

D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference,
and Prediction, 2nd ed., Springer-Verlag, 2009

B. Liu, Web Data Mining, Springer 2006.

T. M. Mitchell, Machine Learning, McGraw Hill, 1997

G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991

P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005

S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998

I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java
Implementations, Morgan Kaufmann, 2nd ed. 2005
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Chapter 1. Introduction
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Why Data Mining?
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What Is Data Mining?
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A Multi-Dimensional View of Data Mining
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What Kind of Data Can Be Mined?
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What Kinds of Patterns Can Be Mined?
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What Technology Are Used?
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What Kind of Applications Are Targeted?
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Major Issues in Data Mining
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A Brief History of Data Mining and Data Mining Society
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Summary
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Summary
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Data mining: Discovering interesting patterns and knowledge from
massive amount of data
A natural evolution of database technology, in great demand, with
wide applications
A KDD process includes data cleaning, data integration, data
selection, transformation, data mining, pattern evaluation, and
knowledge presentation
Mining can be performed in a variety of data
Data mining functionalities: characterization, discrimination,
association, classification, clustering, outlier and trend analysis, etc.
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Data mining technologies and applications
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Major issues in data mining
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Research Frontiers in Advanced Data
Mining Technologies and Applications
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Pattern mining, pattern usage, and pattern understanding
Information network analysis
Stream data mining
Mining moving object data, RFID data, and data from
sensor networks
Spatiotemporal and multimedia data mining
Biological data mining
Text and Web mining
Data mining for software engineering and computer
system analysis
Data cube-oriented multidimensional online analytical
analysis
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Pattern Mining & Its Applications
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Mining approximate and/or colossal patterns
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Open problems for sequential and graph/structural patterns
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Mining sequential patterns in long-sequences in text data
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Mining sequential patterns in long-sequences in biological data
Extracting redundancy-aware top-k patterns
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Pattern-based classification
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Frequent, discriminative pattern analysis
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For numerical and high-dimensional data
Semantic annotation of frequent patterns
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Pattern compression
Patterns become meaningful/interesting in proper contexts
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Information Network Analysis
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Everything is linked!
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Web, social networks, information networks, biological
networks, and physical networks (transportation,
power, compter network, etc.)
Links as endorsement, containing rich semantic
information
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Mining social network by clustering and simplification
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Mining heterogeneous networks
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OLAP information networks
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Evolution of information networks
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Privacy and security in information networks
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Link Analysis and Multi-Relational Mining
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Cross-relational classification
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Cross-relational clustering with user's guidance
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LinkClus: Efficient clustering via heterogeneous semantic
links
Object Distinction: Distinguishing objects with identical
names by link analysis
Veracity analysis: Truth discovery with multiple conflicting
information providers on the Web
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Handling time-evolving truth and multi-versioned truth
RankClus: Integration of Ranking and Clustering in
heterogeneous information networks
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Stream Data Mining
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Stream OLAP and stream data cube
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Stream sample counting and frequent pattern analysis
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Stream sequential and structured pattern analysis
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Classification of data streams
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Classification with skewed data (rare events)
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Classification with partially labeled data
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Clustering data streams
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Stream outlier analysis
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Integrating with new outliler analysis methods
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Mining Moving Object Data, RFID Data,
and traffic data mining
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Mining moving object data
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RFID data warehousing and data mining
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Warehousing and Analysis of Massive RFID Data Sets.
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Cost-conscious Cleaning of Massive RFID Data Sets.
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Trajectory classification, clustering, outlier analysis, and
pattern analysis
FlowCube: Constructuing RFID FlowCubes for MultiDimensional Analysis of Commodity Flows
Traffic mining
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Traffic data cube
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Traffic pattern and outlier analysis
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Spatiotemporal and Multimedia Data
Mining
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Multimodal data: spatial, spatiotemploral, image, video,
text data
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Spatial, spatiotemploral and multimedia data warehouse
and OLAP
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Spatial, spatiotemploral, and multimedia frequent pattern
and correlation analysis
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Spatial, spatiotemploral and multimedia data classification
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Spatial, spatiotemploral and multimedia clustering
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Spatial, spatiotemploral and multimedia outlier analysis
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Biological Data Mining
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Bioinformatics: A driving force in data mining
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Huge, complex and valuable data sources for data mining
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The most rewarding frontier for data mining
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Biological sequence analysis
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Promoters and cis-regulatory analysis
Data mining for genomics and proteomics
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Gene or protein chips, molecular structures, bio-medical data,
spatial/image data,
Classification, clustering, outlier analysis
Biological graph/network mining
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Mining structures, linkages and clusters
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Analysis of biomedical literature databases (MEDLINE, BIOSIS, etc.)
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Text and Web Mining
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Document clustering
Document classification
Document pattern analysis
Text- or Topic- cube and text OLAP
Web analysis
 Web community discovery
 Block-based Web search and block-level link analysis
 Mining web structures
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Data Mining for Software Engineering,
Computer System and Sensor Network Analysis
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Software bug mining
 Statistical Model-based Bug Localization
 Failure Proximity: A Fault Localization-Based Approach
Mining for software plagiarism detection
 Detection of Software Plagiarism by Procedure
Dependency Graph Analysis
Mining for structural indexing and similarity search
 Graph Indexing: A Frequent Structure-based Approach
 Substructure Similarity Search
Sensor Networks
 Multi-stage approach for sensor network program
debugging
Data mining in cyber-physical systems
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Data Cube-Oriented Multidimensional
Online Analytical Processing
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Integration of data cube with data mining
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Regression Cubes
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Prediction Cubes
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Sampling cubes
Integration cube and ranking query processing
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The Ranking Cube Approach
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ArCube: Aggregation ranking in data cubes
High-dimensional OLAP
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Integration with high-dimensional data mining
Multidimensional promotion analysis
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Privacy-Preserving Data Mining
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Mining and privacy: Conflicting goals?
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Powerful data mining tools vs. privacy/security
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Homeland security and individual privacy
Solution—privacy-preserving data mining
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Encryption of data while preserving mining statistics
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Data perturbation: adding noise and randomization
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Data transformation:
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Projection in different angles while preserving
mining results
Privacy-preserving mining in distributed environments
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Invisible Data Mining
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Data mining as an invisible component of information
service
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Web search engine (link analysis, authoritative pages, user
profiles), Google News, adaptive web sites, etc.
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Query optimization via data mining
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Software bug detection and correction
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Improvement of query processing based on history + data
Based on software execution history and statistics
Data mining: hard to learn and master?
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Embedded functions: Best way to deploy data mining solutions
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Making service smart and efficient
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Integrated Data/Information
Systems
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Today’s information systems: DBMS
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Future information systems
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Integration of (semi-structured) database (storage,
access), data warehouse (integration, consolidation),
and data mining (knowledge discovery)
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Infrastructure for integrated data/information
systems
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Dynamically determine which functions to evoke
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Mining query optimization
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System tuning and adaptation by mining
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Applications of Data Mining
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Profit mining, micro-economical model
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Data mining and network intrusion detection
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Botnet detection
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Data mining and collaborative filtering
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Mining network value of customers
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Mining Biological Data: Coverage
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Mining DNA, RNA, and proteins: (1) Mining motif patterns,
(2) searching homology in large databases, (3)
phylogenetic and functional prediction—Exploring
sequential and structural data (e.g., protein structure)
mining methods
Mining gene expression data: (1) clustering gene
expression, e.g., for gene regulatory networks, (2)
classifying gene expression, e.g., for disease-sensitive
gene discovery.
Mining mass spectrometry data:
Mining and integrating knowledge from biomedical
literature
Mining inter-domain associations
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Text Mining
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Text representation
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Shallow linguistics
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agglomerative, k-means, EM; effect of a large number of noise
dimensions, partial supervision
Document classification
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PCA, SVD, latent semantic indexing
Text clustering
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Phrase detection, part-of-speech tagging, named entity extraction,
word sense disambiguation
Feature selection and dimensionality reduction
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Set-of-words, bag-of-words, vector-space model; the issue of large
raw dimensionality
Naive Bayes classification: Poor density estimates, small-degree
Bayesian belief network induction
Discriminative learning: maximum entropy, logistic regression, and
support vector learning
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Mining the Web
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Web modeling
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Classification and clustering
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Information extraction, exploiting markup structure to extract
structured data from pages meant for human consumption
Multidimensional Web databases
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Mining by exploiting text and links for better clustering and
classification; unified probabilistic models for text and links
Structured data extraction
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Web as an evolving, collaborative, social network: graph-structure
of the Web
Data sources: Semi-structured vs. structured, shallow vs. deep
Automatic construction of multilayered Web information base;
discovering entities and relations on the Web
Semantic Web
Web usage mining and adaptive Web sites
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Data Mining & System/Software
Engineering
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System engineering
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Expensive: Construction, maintenance and evolution
of sophisticated systems (esp. software systems)
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Autonomic computing: automate the above process
Software systems generates huge volumes of data
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Abundant and valuable sources for data mining
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Find rules, regularity, alarms for outliers and bugs
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System diagnosis, maintenance, and evolution by data
mining
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Grand Challenges in Data Mining
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Mining sequences, trees, graphs, and complex networks
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Data mining in data streams and sensor databases
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Data mining across multiple, heterogeneous data sources
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Mining in Web and global information systems
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Bio-medical data mining and bioinformatics
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Data mining for software/system engineering
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Mining for data integration and improvement of DBMS
functionality
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Invisible data mining
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Privacy-preserving data mining
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Data mining languages, foundations, and breakthroughs
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