Download Data Mining

Data Mining
Page 1
Syllabus
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Week
Material
Week
Introduction
Week 2
Data Warehouse & OLAP
Week 3
Data Preprocessing
Week 4
Data Mining Languages
Week 5
Concept Description
Week 6
Statistic
Week 7-8 Association Rules
Week 9-10 Classification
Week 11-12 Cluster Analysis
Week 13-14 Mining Complex Data
Week 15
Applications
• Midterm
3/2/04
• Project due
4/29/04
• Final 5/6/04
• No Late
Submissions
are allowed
Page 2
Textbook and Other Reading Materials
• Textbook: Data Mining: Concepts and Techniques
by Jiawei Han and Micheline Kamber, Morgan
Kaufman, 2001
• Other texts that I may use from time to time:
– Data Mining –Introductory and Advanced Topics by
Margaret H. Duhnam, Pearson Education,Inc, 2003
– Principles of Data Mining by David Hand, Heikki Mannila, and
Padhriac Smyth, MIT Press 2001
• Papers: VLDB, SIGMOD, and SIGKDD Proceedings`
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Introduction
• Motivation.
• What is data mining?
• Data mining functionality
• Are all the patterns interesting?
• Classification of data mining systems
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Motivation:
• Huge amount of databases and web pages make information
extraction next to impossible (remember the favored statement: I
will bury them in data!)
• Inability of many other disciplines: (statistic, AI, information
retrieval) to have scalable algorithms to extract information
and/or rules from the databases
• Necessity to find relationships among data
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Appetizer
• Consider a file consisting of 24471 records.
File contains at least two condition attributes:
A and D
A/D
0
1
total
0
9272
232
9504
1
14695 272
14967
Total 23967 504
24471
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Appetizer (con’t)
• Probability that person has A: P(A)=0.6, P(D)=0.02
• Conditional probability that person has D provided it has A:
P(D|A) = P(AD)/P(A)=(272/24471)/.6 = .02
• P(A|D) = P(AD)/P(D)= .56
• What can we say about dependencies between A and D?
A/D
0
1
total
0
9272
232
9504
1
14695 272
14967
Total 23967 504
24471
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Appetizer(3)
• So far we did not ask anything that statistics would not
have ask. So Data Mining another word for statistic?
• We hope that the response will be resounding NO
• The major difference is that statistical methods work
with random data samples, whereas the data in
databases is not necessarily random
• The second difference is the size of the data set
• The third data is that statistical samples do not
contain “dirty” data
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STATISTIC is NOT DATA MINING
• Originally data mining was a statistician term
for overusing data to create possible wrong
inferences.
• Famous example of wrong inferences is in
parapsychology on ECP (extrasensory
perception)
• If there are too many conclusions from the
data, then some will be certainly true.
• Data Mining is a discovery of UNEXPECTED
data correlations
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What Is Data Mining?
• Data mining (knowledge discovery in databases):
– Extraction of interesting information or patterns from data
in large databases
• Alternative names and their “inside stories”:
– Knowledge discovery(mining) in databases (KDD),
knowledge extraction, data/pattern analysis, data
archeology, data dredging, information harvesting,
business intelligence, etc.
• What is not data mining?
–
–
–
–
(Deductive) query processing.
Expert systems or small ML/statistical programs
Statistics
Artificial Intelligence
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Data Mining: Process
Pattern Evaluation
– Data mining: the core of
knowledge discovery
Data Mining
process.
Task-relevant Data
Data Warehouse
Selection
Data Cleaning
Data Integration
Databases
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What Is Data Mining – Steps in the DM
Process
• Data cleaning, noise removal
• Data Integration- data warehousing techniques,
OLAP
• Data Relevancy decision
• Data Transformation (data qube, aggregation and
summarization)
• Pattern evaluations
• Results presentation
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What is DM: Potential Applications
• Database analysis and decision support
– Market analysis and management
• target marketing, customer relation management, market basket
analysis, cross selling, market segmentation
– Risk analysis and management
• Forecasting, customer retention, improved underwriting, quality
control, competitive analysis
– Fraud detection and management
• Other Applications
– Text mining (news group, email, documents) and Web analysis.
– Intelligent query answering
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Market Analysis and Management (1)
• Where are the data sources for analysis?
– Credit card transactions, loyalty cards, discount coupons,
customer complaint calls, plus (public) lifestyle studies
• Target marketing
– Find clusters of “model” customers who share the same
characteristics: interest, income level, spending habits, etc.
• Determine customer purchasing patterns over time
– Conversion of single to a joint bank account: marriage, etc.
• Cross-market analysis
– Associations/co-relations between product sales
– Prediction based on the association information
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Market Analysis and Management (2)
• Customer profiling
– data mining can tell you what types of customers buy what
products (clustering or classification)
• Identifying customer requirements
– identifying the best products for different customers
– use prediction to find what factors will attract new customers
• Provides summary information
– various multidimensional summary reports
– statistical summary information (data central tendency and
variation)
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Corporate Analysis and Risk
Management
• Finance planning and asset evaluation
– cash flow analysis and prediction
– contingent claim analysis to evaluate assets
– cross-sectional and time series analysis (financial-ratio, trend
analysis, etc.)
• Resource planning:
– summarize and compare the resources and spending
• Competition:
– monitor competitors and market directions
– group customers into classes and a class-based pricing
procedure
– set pricing strategy in a highly competitive market
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Fraud Detection and Management (1)
• Applications
– widely used in health care, retail, credit card services,
telecommunications (phone card fraud), etc.
• Approach
– use historical data to build models of fraudulent behavior and
use data mining to help identify similar instances
• Examples
– auto insurance: detect a group of people who stage accidents to
collect on insurance
– money laundering: detect suspicious money transactions (US
Treasury's Financial Crimes Enforcement Network)
– medical insurance: detect professional patients and ring of
doctors and ring of references
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Fraud Detection and Management (2)
• Detecting inappropriate medical treatment
• Detecting telephone fraud
– Telephone call model: destination of the call, duration,
time of day or week. Analyze patterns that deviate
from an expected norm.
– British Telecom identified discrete groups of callers
with frequent intra-group calls, especially mobile
phones, and broke a multimillion dollar fraud.
• Retail
– Analysts estimate that 38% of retail shrink is due to
dishonest employees.
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Other Applications
• Sports
– IBM Advanced Scout analyzed NBA game statistics (shots
blocked, assists, and fouls) to gain competitive advantage for
New York Knicks and Miami Heat
• Astronomy
– JPL and the Palomar Observatory discovered 22 quasars with
the help of data mining
• Internet Web Surf-Aid
– IBM Surf-Aid applies data mining algorithms to Web access
logs for market-related pages to discover customer preference
and behavior pages, analyzing effectiveness of Web marketing,
improving Web site organization, etc.
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Architecture of a Typical Data Mining
System
Graphical user interface
Pattern evaluation
Data mining engine
Knowledge-base
Database or data
warehouse server
Data cleaning & data integration
Databases
Filtering
Data
Warehouse
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Data Mining System Architecture
• Database, data warehouse, data files- set of data to be mined.
Data Cleaning and data integration may be performed at this
stage
• Database or data warehouse server is responsible for fetching
relevant data. How to define relevancy?
• Knowledge Base – Domain knowledge that drives a search for
patterns. Concept hierarchy, User Beliefs, Interestingness
Constraints
• Data Mining Engine-Functional algorithms to perform a search
for domain experts
• Pattern Evaluation – Use knowledge base and other methods to
narrow search for domain patters
• GUI – Communicator between users and data mining system
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Data Mining: On What Kind of Data?
• Relational databases – Universal relation vs
Multirelational search
• Data warehouses
• Transactional databases
• Advanced DB and information repositories
–
–
–
–
–
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Object-oriented and object-relational databases
Spatial databases
Time-series data and temporal data
Text databases and multimedia databases
Heterogeneous and legacy databases
WWW
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Data Mining: On What Kind of Data?
• Attribute Types:
– Categorical – attribute that has a finite number of values
– Ordinal – attributes can be ordered by their values
• Attribute Transformations:
– Continuing - attribute that may have infinite but countable set of
values. These attributes always can be ordered
– Interval scale
– Boolean
• Nominal – attributes that cannot be ordered by their values
– Operational - example measurement of programming
productivity as am(n+m)log(a+b)/2b, where a is the number of
unique operators,b is the number of unique operands, n-number
of total operators occurences and m the number of total
operands occurences
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Data Mining Tasks
• Association (correlation and causality)
– Multi-dimensional vs. single-dimensional association
– age(X, “20..29”) ^ income(X, “20..29K”) -> buys(X, “PC”) [support
= 2%, confidence = 60%]
– contains(T, “computer”) -> contains(x, “software”) [1%, 75%]
– What is support? – the percentage of the tuples in the database
that have age between 20 and 29 and income between 20K and
29K and buying PC
– What is confidence? – the probability that if person is between 20
and 29 and income between 20K and 29K then it buys PC
• Clustering (getting data that are close together into the same
cluster.
• What does “close together” means?
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Distances between data
• Distance between data is a measure of dissimilarity
between data.
d(i,j)>=0; d(i,j) = d(j,i); d(i,j)<= d(i,k) + d(k,j)
• Euclidean distance: <x1,x2, … xk> and <y1,y2,…yk>
• Standardize variables by finding standard deviation and
dividing each xi by standard deviation of X
• Covariance(X,Y)=1/k(Sum(xi-mean(x))(y(I)-mean(y))
• Boolean variables and their distances
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Data Mining Tasks
• Outlier analysis
– Outlier: a data object that does not comply with the general behavior of
the data
– It can be considered as noise or exception but is quite useful in fraud
detection, rare events analysis
• Trend and evolution analysis
– Trend and deviation: regression analysis
– Sequential pattern mining, periodicity analysis
– Similarity-based analysis
• Other pattern-directed or statistical analyses
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Are All the “Discovered” Patterns
Interesting?
• A data mining system/query may generate thousands of patterns,
not all of them are interesting.
– Suggested approach: Human-centered, query-based, focused mining
• Interestingness measures: A pattern is interesting if it is easily
understood by humans, valid on new or test data with some degree
of certainty, potentially useful, novel, or validates some hypothesis
that a user seeks to confirm
• Objective vs. subjective interestingness measures:
– Objective: based on statistics and structures of patterns, e.g., support,
confidence, etc.
– Subjective: based on user’s belief in the data, e.g., unexpectedness,
novelty, actionability, etc.
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Are All the “Discovered” Patterns
Interesting? - Example
coffee
0
1
0
5
70
1
5
tea
20
75
25
Conditional probability that if one buys coffee, one also buys tea
is 2/9
Conditional probability that if one buys tea she also buys coffee
is 20/25=.8
However, the probability that she buys coffee is .9
So, is it significant inference that if customer buys tea she also buys
coffee?
Is buying tea and coffee independent activities?
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How to measure Interestingness
• RI = | X , Y| - |X||Y|/N
• Support and Confidence: |X Y|/N – support and |X Y|/|X| confidence of X->Y
• Chi^2: (|XY| - E(|XY|)) ^2 /E(|XY|);
• J(X->Y) = P(Y)(P(X|Y)*log (P(X|Y)/P(X)) + (1- P(X|Y))*log ((1P(X|Y)/(1-P(X))
• Sufficiency (X->Y) = P(X|Y)/P(X|!Y); Necessity (X->Y) =
P(!X|Y)/P(!X|!Y). Interestingness of Y->X is
NC++ = 1-N(X->Y)*P(Y), if N(…) is less than 1 or 0 otherwise
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Can We Find All and Only Interesting
Patterns?
• Find all the interesting patterns: Completeness
– Can a data mining system find all the interesting patterns?
– Association vs. classification vs. clustering
• Search for only interesting patterns: Optimization
– Can a data mining system find only the interesting patterns?
– Approaches
• First general all the patterns and then filter out the uninteresting
ones.
• Generate only the interesting patterns—mining query optimization
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A Multi-Dimensional View of Data Mining
Classification
• Databases to be mined
– Relational, transactional, object-oriented, object-relational,
active, spatial, time-series, text, multi-media, heterogeneous,
legacy, WWW, etc.
• Knowledge to be mined
– Characterization, discrimination, association, classification,
clustering, trend, deviation and outlier analysis, etc.
– Multiple/integrated functions and mining at multiple levels
• Techniques utilized
– Database-oriented, data warehouse (OLAP), machine learning,
statistics, visualization, neural network, etc.
• Applications adapted
– Retail, telecommunication, banking, fraud analysis, DNA mining, stock
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market analysis, Web mining, Weblog analysis, etc.
OLAP Mining: An Integration of Data Mining
and Data Warehousing
• Data mining systems, DBMS, Data warehouse
systems coupling
– No coupling, loose-coupling, semi-tight-coupling, tight-coupling
• On-line analytical mining data
– integration of mining and OLAP technologies
• Interactive mining multi-level knowledge
– Necessity of mining knowledge and patterns at different levels of
abstraction by drilling/rolling, pivoting, slicing/dicing, etc.
• Integration of multiple mining functions
– Characterized classification, first clustering and then association
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An OLAM Architecture
Mining query
Mining result
Layer4
User Interface
User GUI API
OLAM
Engine
OLAP
Engine
Layer3
OLAP/OLAM
Data Cube API
Layer2
MDDB
MDDB
Meta Data
Filtering&Integration
Database API
Filtering
Layer1
Data cleaning
Databases
Data
Data integration Warehouse
Data
Repository
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Major Issues in Data Mining (1)
• Mining methodology and user interaction
– Mining different kinds of knowledge in databases
– Interactive mining of knowledge at multiple levels of abstraction
– Incorporation of background knowledge
– Data mining query languages and ad-hoc data mining
– Expression and visualization of data mining results
– Handling noise and incomplete data
– Pattern evaluation: the interestingness problem
• Performance and scalability
– Efficiency and scalability of data mining algorithms
– Parallel, distributed and incremental mining methods
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Major Issues in Data Mining (2)
• Issues relating to the diversity of data types
– Handling relational and complex types of data
– Mining information from heterogeneous databases and global
information systems (WWW)
• Issues related to applications and social impacts
– Application of discovered knowledge
• Domain-specific data mining tools
• Intelligent query answering
• Process control and decision making
– Integration of the discovered knowledge with existing knowledge:
A knowledge fusion problem
– Protection of data security, integrity, and privacy
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Summary
• Data mining: discovering interesting patterns from large amounts 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 information repositories
• Data mining functionalities: characterization, discrimination,
association, classification, clustering, outlier and trend analysis, etc.
• Classification of data mining systems
• Major issues in data mining
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