Data Mining

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Data Mining
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Syllabus

• 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

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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 (cont)

• Probability that person has A P(A)0.6,
  P(D)0.02
• Conditional probability that person has D
  provided it has A P(DA) P(AD)/P(A)(272/24471)
  /.6 .02
• P(AD) 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
Knowledge
Pattern Evaluation

• Data mining the core of knowledge discovery
  process.

Data Mining
Task-relevant Data
Selection
Data Warehouse
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
Filtering
Data cleaning data integration
Data Warehouse
Databases
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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
• 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(nm)log(ab)/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) -gt
  buys(X, PC) support 2, confidence 60
• contains(T, computer) -gt 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)gt0 d(i,j) d(j,i) d(i,j)lt d(i,k)
  d(k,j)
• Euclidean distance ltx1,x2, xkgt and lty1,y2,ykgt
• 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 users belief in the data,
  e.g., unexpectedness, novelty, actionability, etc.

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Are All the Discovered Patterns Interesting? -
Example


1
coffee
0 1
tea
5 5 20
25
0
70 75
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 - XY/N
• Support and Confidence X Y/N support and
  X Y/X -confidence of X-gtY
• Chi2 (XY - E(XY)) 2 /E(XY)
• J(X-gtY) P(Y)(P(XY)log (P(XY)/P(X)) (1-
  P(XY))log ((1- P(XY)/(1-P(X))
• Sufficiency (X-gtY) P(XY)/P(X!Y) Necessity
  (X-gtY) P(!XY)/P(!X!Y). Interestingness of
  Y-gtX is
• NC 1-N(X-gtY)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 patternsmining
  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 market analysis, Web
  mining, Weblog analysis, etc.

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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
Layer4 User Interface
Mining query
Mining result
User GUI API
OLAM Engine
OLAP Engine
Layer3 OLAP/OLAM
Data Cube API
Layer2 MDDB
MDDB
Meta Data
Database API
FilteringIntegration
Filtering
Layer1 Data Repository
Data Warehouse
Data cleaning
Databases
Data integration
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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