Introduction data mining

Introduction

 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

October 6, 2012 Data Mining: Concepts and Techniques 1

Motivation: “Necessity is the
Mother of Invention”

 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

 We are drowning in data, but starving for knowledge!
 Solution: Data warehousing and data mining

Data warehousing and on-line analytical processing

 Extraction of interesting knowledge (rules, regularities, patterns,
October 6, 2012 constraints) from dataMining: Concepts and Techniques
Data in large databases 2

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.)
 1990s—2000s:
 Data mining and data warehousing, multimedia databases, and
Web databases

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, data dredging, information harvesting,
business intelligence, etc.
 What is not data mining?
 (Deductive) query processing.
 Expert systems or small ML/statistical programs

Why Data Mining? — 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


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

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)

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


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

Fraud Detection and Management (2)
 Detecting inappropriate medical treatment
 Australian Health Insurance Commission identifies that in many
cases blanket screening tests were requested (save Australian
$1m/yr).
 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.

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.

Data Mining: A KDD Process

Pattern Evaluation
 Data mining: the core of
knowledge discovery
process. Data Mining

Task-relevant Data

Data Selection
Warehouse
Data Cleaning

Data Integration

Databases

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

Data Mining and Business
Intelligence
Increasing potential
to support
business decisions End User
Making
Decisions

Data Presentation Business
Analyst
Visualization Techniques
Data Mining Data
Information Discovery Analyst

Data Exploration
Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts
OLAP, MDA DBA
Data Sources
Paper, Files, Information Providers, Database Systems, OLTP

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
Databases Warehouse


Data Mining: On What Kind of
Data?

 Relational databases
 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

Data Mining Functionalities (1)

 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”)  buys(X,
“PC”) [support = 2%, confidence = 60%]
 contains(T, “computer”)  contains(x, “software”) [1%,
75%]


 Classification and Prediction
 Finding models (functions) that describe and distinguish classes
or concepts for future prediction
 E.g., classify countries based on climate, or classify cars based
on gas mileage
 Presentation: decision-tree, classification rule, neural network
 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 based on the principle: maximizing the intra-class
similarity and minimizing the interclass similarity


 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

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.

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

Data Mining: Confluence of Multiple
Disciplines
Database
Statistics
Technology

Machine
Learning
Data Mining Visualization

Information Other
Science Disciplines


Introduction data mining

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