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Introduction to Data Mining ... telecommunication, banking, fraud analysis, DNA mining, stock market analysis, Web mining, Weblog analysis, ... – PowerPoint PPT presentation

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Title: Temple University


1
Temple University CIS Dept.CIS616 Principles
of Data Management
  • V. Megalooikonomou
  • Introduction to Data Mining
  • (based on notes by Jiawei Han and Micheline
    Kamber and on notes by Christos Faloutsos)

2
General Overview - rel. model
  • Relational model - SQL
  • Functional Dependencies Normalization
  • Physical Design Indexing
  • Query processing/optimization
  • Transaction processing
  • Advanced topics
  • Distributed Databases
  • OO- and OR-DBMSs
  • Data Mining

3
Agenda
  • Motivation Why data mining?
  • What is data mining?
  • Data Mining On what kind of data?
  • Data mining functionality
  • Are all the patterns interesting?
  • Classification of data mining systems
  • Major issues in data mining

4
Motivation
  • Data rich but information poor!
  • Data explosion problem
  • Automated data collection tools and mature
    database technology lead to tremendous amounts of
    data stored in databases, data warehouses and
    other information repositories
  • Solution Data Mining
  • Extraction of interesting knowledge (rules,
    regularities, patterns, constraints) from data
    in large databases

5
Evolution of Database Technology
  • 1960s
  • Data collection, database creation, IMS and
    network DBMS
  • 1970s
  • Relational data model, relational DBMS
    implementation
  • 1980s
  • RDBMS, advanced data models (extended-relational,
    OO, deductive, etc.) and application-oriented
    DBMS (spatial, scientific, engineering, etc.)
  • 1990s2000s
  • Data mining and data warehousing, multimedia
    databases, and Web databases

6
What Is Data Mining?
  • Data mining (knowledge discovery in databases)
  • Extraction of interesting (non-trivial, implicit,
    previously unknown and potentially useful)
    information or patterns from data in large
    databases
  • Alternative names
  • Knowledge discovery(mining) in databases (KDD),
    knowledge extraction, data/pattern analysis, data
    archeology, information harvesting, business
    intelligence, etc.
  • What is not data mining?
  • (Deductive) query processing.
  • Expert systems or small ML/statistical programs

7
What Is Data Mining?
  • Now that we have gathered so much data,

what do we do with it?
  • Extract interesting patterns (automatically)
  • Associations (e.g., butter bread --gt milk)
  • Sequences (e.g., temporal data related to stock
    market)
  • Rules that partition the data (e.g., store
    location problem)
  • What patterns are interesting?

information content, confidence and support,
unexpectedness, actionability (utility in
decision making))
8
Why Data Mining? Potential Applications
  • Database analysis and decision support
  • Market analysis and management
  • target marketing, market basket analysis,
  • Risk analysis and management
  • Forecasting, quality control, competitive
    analysis,
  • Fraud detection and management
  • Other Applications
  • Text mining (newsgroup, email, documents) and Web
    analysis.
  • Spatial data mining
  • Intelligent query answering

9
Market Analysis and Management
  • Data sources for analysis? (Credit card
    transactions, discount coupons, customer
    complaint calls, etc.)
  • Target marketing (Find clusters of model
    customers who share same characteristics
    interest, income level, spending habits, etc.)
  • Customer purchasing patterns over time
    (Conversion of single to a joint bank account
    marriage, etc.)
  • Cross-market analysis (Associations between
    product sales and prediction based on
    associations)
  • Customer Profiling (What customers buy what
    products)
  • Customer Requirements (Best products for
    different customers)
  • Summary information (multidimensional summary
    reports)

10
Risk Analysis and Management
  • Finance planning and asset evaluation
  • cash flow analysis and prediction
  • 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

11
Fraud Detection and Management
  • Applications
  • health care, retail, credit card services,
    telecommunications etc.
  • Approach
  • use historical data to build models of normal and
    fraudulent behavior and use data mining to help
    identify fraudulent instances
  • Examples
  • auto insurance detect groups who stage accidents
    to collect insurance
  • money laundering detect suspicious money
    transactions
  • medical insurance detect professional patients
    and ring of doctors, inappropriate medical
    treatment
  • detecting telephone fraudTelephone call model
    destination of the call, duration, time of
    day/week. Analyze patterns that deviate from
    expected norm.

12
Discovery of Medical/Biological Knowledge
  • Discovery of structure-function associations
  • Structure of proteins and their function
  • Human Brain Mapping (lesion-deficit,
    task-activation associations)
  • Cell structure (cytoskeleton) and functionality
    or pathology
  • Discovery of causal relationships
  • Symptoms and medical conditions
  • DNA sequence analysis
  • Bioinformatics (microarrays, etc)

13
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.

14
Data Mining A KDD 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
15
Steps of a KDD Process
  • Learning the application domain
  • relevant prior knowledge and goals of application
  • Creating a target data set data selection
  • Data cleaning and preprocessing (may take 60 of
    effort!)
  • Data reduction and transformation
  • Find useful features, dimensionality/variable
    reduction, invariant representation.
  • Choosing functions of data mining
  • summarization, classification, regression,
    association, clustering.
  • Choosing the mining algorithm(s)
  • Data mining search for patterns of interest
  • Pattern evaluation and knowledge presentation
  • visualization, transformation, removing redundant
    patterns, etc.
  • Use of discovered knowledge

16
Data Mining and Business Intelligence
End User
Making Decisions
Increasing potential to support business decisions
Business Analyst
Data Presentation
Visualization Techniques
Data Mining
Data Analyst
Information Discovery
Data Exploration
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
OLAP, MDA
DBA
Data Sources
Paper, Files, Information Providers, Database
Systems
17
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
Filtering
Data Warehouse
Databases
18
Data Mining On What Kind of Data?
  • Relational databases
  • Data warehouses
  • Transactional databases
  • Advanced DB and information repositories
  • Object-oriented (OO)and object-relational (OR)
    databases
  • Spatial databases (medical, satellite image DBs,
    GIS)
  • Temporal databases
  • Text databases
  • Multimedia databases (Image, Video, etc)
  • Heterogeneous and legacy databases
  • WWW

19
Data Mining Functionalities Patterns that can
be mined
  • Concept description Characterization and
    discrimination
  • Generalize, summarize, and contrast data
    characteristics, e.g., dry vs. wet regions
  • Association (correlation and causality)
  • Multi-dimensional vs. single-dimensional
    association
  • age(X, 20..29) income(X, 20..29K) à buys(X,
    PC) support 2, confidence 60
  • contains(T, computer) à contains(x, software)
    1, 75
  • Confidence(x à y) P(yx) degree of certainty
    of association
  • Support(x à y) P(x ?y) of transactions that
    the rule satisfies

20
Data Mining Functionalities Patterns that can
be mined
  • Classification and Prediction
  • Finding models (e.g., if-then rules, decision
    trees, mathematical formulae, neural networks,
    classification rules) that describe and
    distinguish classes or concepts for future
    prediction, e.g., classify cars based on
    gasmileage
  • Prediction Predict some unknown or missing
    numerical values
  • Cluster analysis
  • Class label is unknown Group data to form new
    classes, e.g., cluster houses to find
    distribution patterns
  • Clustering principle maximize intra-class
    similarity and minimize interclass similarity

21
Data Mining Functionalities Patterns that can
be mined
  • Outlier analysis
  • Outliers data objects that do not comply with
    the general behavior of the data (can be detected
    using statistical tests that assume a prob.
    model)
  • Often considered as noise but useful in fraud
    detection, rare events analysis
  • Trend and evolution analysis
  • Study regularities of objects whose behavior
    changes over time
  • Trend and deviation regression analysis
  • Sequential pattern mining, periodicity analysis
  • Similarity-based analysis

22
When is a Discovered Pattern 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.

23
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 generate all the patterns and then filter
    out the uninteresting ones
  • Generate only the interesting patternsmining
    query optimization

24
Data Mining Confluence of Multiple Disciplines
Database Technology
Statistics
Data Mining
Machine Learning
Visualization
Information Science
Other Disciplines
25
Data Mining Classification Schemes
  • General functionality
  • Descriptive data mining
  • Predictive data mining
  • Different views, different classifications
  • Kinds of databases to be mined
  • Kinds of knowledge to be discovered
  • Kinds of techniques utilized
  • Kinds of applications adapted

26
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.

27
Major Issues in Data Mining
  • 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 to guide
    the discovery process
  • 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

28
More details Decision Trees
  • Decision trees
  • Problem
  • Approach
  • Classification through trees
  • Building phase - splitting policies
  • Pruning phase (to avoid over-fitting)

29
Decision Trees
  • Problem Classification i.e., given a training
    set (N tuples, with M attributes, plus a label
    attribute) find rules, to predict the label for
    newcomers
  • Pictorially

30
Decision trees
??
31
Decision trees
  • Issues
  • missing values
  • noise
  • rare events

32
Decision trees
  • types of attributes
  • numerical ( continuous) - eg salary
  • ordinal ( integer) - eg. of children
  • nominal ( categorical) - eg. car-type

33
Decision trees
  • Pictorially, we have

34
Decision trees
  • and we want to label ?

?
num. attr2 (e.g., chol-level)
-
-



-

-

-

-

num. attr1 (e.g., age)
35
Decision trees
  • so we build a decision tree

?
num. attr2 (e.g., chol-level)
-
-



40
-

-

-

-

50
num. attr1 (e.g., age)
36
Decision trees
  • so we build a decision tree

agelt50
N
Y
chol. lt40

Y
N
-
...
37
Decision trees
  • Typically, two steps
  • tree building
  • tree pruning (for over-training/over-fitting)

38
Tree building
  • How?

39
Tree building
  • How?
  • A Partition, recursively - pseudocode
  • Partition ( dataset S)
  • if all points in S have same label
  • then return
  • evaluate splits along each attribute A
  • pick best split, to divide S into S1 and S2
  • Partition(S1) Partition(S2)
  • Q1 how to introduce splits along attribute Ai
  • Q2 how to evaluate a split?

40
Tree building
  • Q1 how to introduce splits along attribute Ai
  • A1
  • for numerical attributes
  • binary split, or
  • multiple split
  • for categorical attributes
  • compute all subsets (expensive!), or
  • use a greedy approach

41
Tree building
  • Q2 how to evaluate a split?
  • A by how close to uniform each subset is - ie.,
    we need a measure of uniformity

42
Tree building
Any other measure?
entropy H(p, p-)
p relative frequency of class in S
1
0
0.5
0
1
p
43
Tree building
gini index 1-p2 - p-2
entropy H(p, p-)
p relative frequency of class in S
1
0
0.5
0
1
p
44
Tree building
  • Intuition
  • entropy bits to encode the class label
  • gini classification error, if we randomly guess
    with prob. p

45
Tree building
  • Thus, we choose the split that reduces
    entropy/classification-error the most E.g.

46
Tree building
  • Before split we need
  • (n n-) H( p, p-) (76) H(7/13, 6/13)
  • bits total, to encode all the class labels
  • After the split we need
  • 0 bits for the
    first half and
  • (26) H(2/8, 6/8) bits for the second half

47
Tree pruning
  • What for?

48
Tree pruning
  • Q How to do it?

49
Tree pruning
  • Q How to do it?
  • A1 use a training and a testing set - prune
    nodes that improve classification in the
    testing set. (Drawbacks?)

50
Tree pruning
  • Q How to do it?
  • A1 use a training and a testing set - prune
    nodes that improve classification in the
    testing set. (Drawbacks?)
  • A2 or, rely on MDL ( Minimum Description
    Language) - in detail

51
Tree pruning
  • envision the problem as compression
  • and try to minimize the bits to compress
  • (a) the class labels AND
  • (b) the representation of the decision tree

52
(MDL)
  • a brilliant idea e.g. best n-degree polynomial
    to compress these points
  • the one that minimizes (sum of errors n )

53
Major Issues in Data Mining
  • Issues relating to the diversity of data types
  • Handling relational as well as 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

54
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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