Download Data mining(DM)

Data Mining : The Discovery Technology for
Knowledge Management
Yike Guo
Dept. of Computing
Imperial College
Advanced Technology for Knowledge Management
Course Overview
• Goal
– Basic Concepts of Data Mining
– Basic Data Mining Techniques
– Data Mining procedure in Real World Applications
– Future Research Trends on Data Mining
• Reference Books
• Advances in Knowledge Discovery and Data Mining U.M
Fayyad and G, Piatetsky-Shapiro AAAI/MIT Press. 1996
• Predictive Data Mining: A Practical Guide Sholom M.Weiss and
Nitin Indurkhya Morgan Kaufmann Publishers, Inc. 1997
•
Data Mining Techniques Wiley Computer Publishing, 1997
Advanced Technology for Knowledge Management
What does the data say?
Day
Outlook Temperature
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Sunny
Sunny
Overcast
Rain
Rain
Rain
Overcast
Sunny
Sunny
Rain
Sunny
Overcast
Overcast
Rain
Hot
Hot
Hot
Mild
Cool
Cool
Cool
Mild
Cool
Mild
Mild
Mild
Hot
Mild
Humidity
High
High
High
High
Normal
Normal
Normal
High
Normal
Normal
Normal
High
Normal
High
Wind
Play Tennis
Weak
Strong
Weak
Weak
Weak
Strong
Strong
Weak
Weak
Weak
Strong
Strong
Weak
Strong
No
No
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
No
Advanced Technology for Knowledge Management
Turing Data into Knowledge
Advanced Technology for Knowledge Management
Data Mining
Machine Learning
Statistics
Decision Support
Enabling Technology
Knowledge Discovery
Data Mining
Databases
Infrastructure
High
Performance
& Distributed
Computing
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Why Data Mining
• Limitation of traditional database querying:
– Most queries of interest to data owners are difficult to
state in a query language
• “ find me all records indicating fraud”=> “ tell me the
characteristics of fraud” (Summarisation)
• “find me who likely to buy product X” (classification
problem)
• “find all records that are similar to records in table X”
(clustering problem)
– Ability to support analysis and decision making using
traditional (SQL) queries become infeasible (query
formulation problem ).
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Relational Database Revisited
• Terabyte databases, consisting of billions of records,
are becoming common
• Relational data model is the defacto standard
• A relational database : set of relations
• A relation : a set of homogenous tuples
• Relations are created, updated and queried using SQL
• Query = Keyword based search
SELECT telephone_number
FROM telephone_book
WHERE last_name = “Smith”
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SQL : Relational Querying Language
• Provides a well-defined set of operations: scan, join,
insert, delete, sort, aggregate, union, difference
• Scan -- applies a predicate P to relation R
For each tuple tr from R
if P(tr) is true, tr is inserted in the output stream
• Join -- composes two relations R and S
For each tuple tr from R
For each tuple ts from S
if join attribute of tr equals to join attribute of ts
form output tuple by concatenating tr and ts
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The Query Formulation Problem
Consider the query :
What kinds of weather condition are suitable for
playing tennis ?
• It is not solvable via query optimisation
• Has not received much attention in the database
field or in traditional statistical approaches
• These problems are of inductive features: learning
from data rather than search from data
• Natural solution is via train-by-example approach
to construct inductive models as the answers
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Why Data Mining Now
• Data Explosion
– Business Data : organisations such as supermarket chains, credit
card companies, investment banks, government agencies, etc.
routinely generate daily volumes of 100MB of data
– Scientific Data: Scientific and remote sensing instruments collect
data at the rates of Gigabytes per day: far beyond human analysis
abilities.
• Data Wasting
– Only a small portion (5% - 10%) of the collected data is ever
analysed
– Data that may never be analysed continues to be collected, at great
expense.
• We are drowning in data, but starving for knowledge!
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What is Data Mining
Data Mining: a non-trivial data analysis process for
identifying valid, useful and understandable patterns from
databases.
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• Data: set of facts F ( records in a database)
• Pattern : An expression E in a language L describing data in
a subset FE of F and E is simpler than the enumeration of al l
the facts of FE. FE is also called a class and E is also
called a model or knowledge.
• Data Mining Process: data mining is a multi-step process
involving multiple choices, iteration and evaluation. It is nontrivial since there is no closed-form solution. It always involve
intensive search.
• Validity : E is true (with high probability) for F
• Useful : patterns are not trivial inductive properties of data
• Understandable: patterns should be understandable by data
owners to aid in understanding the data/domain
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How Data Mining Works
Data
Knowledge
Data
Mining
System
Decision
Support System
Historical Data
(Data Warehouse)
Feedback
Operational Data
Decision
Evaluation
Business
Predictive
Intelligence
Models
Action
Advanced Business
Technology for
Knowledge Management
Data Warehousing
• “ A data warehouse is a subject-oriented, integrated, time-variant,
and nonvolatile collection of data in support of management’s
decision-making process.” --- W. H. Inmon
• A data warehouse is
– A decision support database that is maintained separately from
the organization’s operational databases.
– It integrates data from multiple heterogeneous sources to
support the continuing need for structured and /or ad-hoc
queries, analytical reporting, and decision support.
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Modeling Data Warehouses
• Modeling data warehouses: dimensions & measurements
– Star schema: A single object (fact table) in the middle
connected to a number of objects (dimension tables) radically.
– Snowflake schema: A refinement of star schema where the
dimensional hierarchy is represented explicitly by normalizing
the dimension tables.
– Fact constellations: Multiple fact tables share dimension
tables.
• Storage of selected summary tables:
– Independent summary table storing pre-aggregated data, e.g.,
total sales by product by year.
– Encoding aggregated tuples in the same fact table and the
same dimension tables.
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Example of Star Schema
Time Dimension Table
Sales Fact Table
Product Dimension Table
Many Time Attributes
Time_Key
Many Product Attributes
Product_Key
Store Dimension Table
Many Store Attributes
Store_Key
Location_Key
Location Dimension Table
Many Location Attributes
unit_sales
Measures
dollar_sales
Yen_sales
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Example of a Snowflake Schema
Supplier_Key
Time Dimension Table
Many Time Attributes
Store Dimension Table
Many Store Attributes
Sales Fact Table
Product Dimension Table
Time_Key
Supplier_Key
Product_Key
Product_Key
Store_Key
Location_Key
Location Dimension Table
Location_Key
unit_sales
Measures
dollar_sales
Country
Location_Key
Yen_sales
Region
Location_Key
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A Star-Net Query Model
Customer Orders
Shipping Method
Customer
CONTRACTS
AIR-EXPRESS
ORDER
TRUCK
PRODUCT LINE
Time
Product
ANNUALY QTRLY
DAILY
PRODUCT ITEM PRODUCT GROUP
DISTRICT
SALES PERSON
REGION
DISTRICT
COUNTRY
DIVISION
Geography
Promotion
Organization
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View of Warehouses and Hierarchies
• Importing data
• Table Browsing
• Dimension creation
• Dimension browsing
• Cube building
• Cube browsing
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Construction of Data Cubes
Amount
B.C.
Province Prairies
Ontario
sum
0-20K20-40K 40-60K60K- sum
All Amount
Comp_Method, B.C.
Comp_Method
Database
… ...
Discipline
sum
Each dimension contains a hierarchy of values for one attribute
A cube cell stores aggregate values, e.g., count, sum, max, etc.
A “sum” cell stores dimension summation values.
Sparse-cube technology and MOLAP/ROLAP integration.
“Chunk”-based multi-way aggregation and single-pass computation.
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OLAP: On-Line Analytical Processing
• A multidimensional, LOGICAL view of the data.
• Interactive analysis of the data: drill, pivot, slice_dice, filter.
• Summarization and aggregations at every dimension
intersection.
• Retrieval and display of data in 2-D or 3-D crosstabs, charts,
and graphs, with easy pivoting of the axes.
• Analytical modeling: deriving ratios, variance, etc. and
involving measurements or numerical data across many
dimensions.
• Forecasting, trend analysis, and statistical analysis.
• Requirement: Quick response to OLAP queries.
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OLAP Architecture
• Logical architecture:
– OLAP view: multidimensional and logic presentation of
the data in the data warehouse/mart to the business user.
– Data store technology: The technology options of how and
where the data is stored.
• Three services components:
– data store services
– OLAP services, and
– user presentation services.
• Two data store architectures:
– Multidimensional data store: (MOLAP).
– Relational data store: Relational OLAP (ROLAP).
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Dimension Browsing
• Product
<======
• Location
======>
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Decision Support with Data Warehouse
• Ad Hoc Queries: Q: How many customers do we
have in London? A: 32776
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• Report and Spreadsheet
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• OLAP: Q:What are the sales figures for Y in the
different regions:
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• Statistics: Q: Is there a relation between age and
buy behaviour? A: Older clients buy more
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• Data Mining: Q: What factors influence buying
behaviour ?
A1: : Young men in sports cars buy 3
times as much audio equipment
(clustering/regression):
Age
A2: Older woman with dark hair more
often buy rinse (classification)
Old
Hair color
B
A3: Buyers of cars are also the buyers
of houses (asociation)
Young
Middle
Y
Wage
N
W
L
N
N
H
Y
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Example Data Mining Applications
• Commercial :
–
–
–
–
Fraud detection: Identify Fraudulent transaction
Loan approval: Establish the credit worthiness of a customer requesting a loan
Investment analysis : Predict a portfolio's return on investment
Marketing and sales data analysis: Identify potential customers; establishing the
effectiveness of a sales campaign
• Medical:
– Drug effect analysis : from patient records to learn drug effects
– Disease causality analysis
• Political policy:
– Election policy : people’s voting patterns
– Social policy: tax/benefit policy
• Manufacturing:
– Manufacturing process analysis: identify the causes of manufacturing problems
– Experiment result analysis : Summarise experiment results and create predictive
models
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• Scientific data analysis:
cataloguing in surveys, basic processing needed before higher-level science
analysis can occur, scientific discovery over large data sets.
Data Mining
(Statistical Computing and Machine Learning)
Theory
Numerical Computing
(Iterative Equation Solving)
Experiments
Simulation
Data Assimilation
(Data Warehousing)
Numerical Computing : simulating the real world systems based on the underlying theory
Data Assimilation :comprehending, consolidating and warehousing the simulation/experiment data
Data Mining : analysis the warehoused simulation/experiment data for knowledge discovery
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Related Fields:
• Machine learning: Inductive reasoning
• Statistics : Sampling, Statistical Inference, Error
Estimation
• Pattern recognition: Neural Networks, Clustering
• Knowledge Acquisition, Statistical Expert Systems
• Data Visualisation
• Databases: OLAP, Parallel DBMS, Deductive
Databases
• Data Warehousing: collection, cleaning of
transactional data for on-line retrial
Advanced Technology for Knowledge Management