Data ware house and miningUNIT-1 DATA MINING CONCEPT.ppt

UNIT--I
December 18, 2024 Data Mining: Concepts and Techniques 1

UNIT-1. 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

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,
constraints) from data in large databases

Evolution of Database Technology
(See Fig. 1.1)
 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 and their “inside stories”:
 Data mining: a misnomer?
 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
 Data mining: the core of
knowledge discovery
process.
Data Cleaning
Data Integration
Databases
Data
Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation

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
Business
Analyst
Data
Analyst
DBA
Making
Decisions
Data Presentation
Visualization Techniques
Data Mining
Information Discovery
Data Exploration
OLAP, MDA
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
Data Sources
Paper, Files, Information Providers, Database Systems, OLTP

Architecture of a Typical Data
Mining System
Data
Warehouse
Data cleaning & data integration Filtering
Databases
Database or data
warehouse
server
Data mining engine
Pattern evaluation
Graphical user interface
Knowledge-base

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
Data Mining
Database
Technology
Statistics
Other
Disciplines
Information
Science
Machine
Learning
Visualization

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

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.

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

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

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

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

Data ware house and miningUNIT-1 DATA MINING CONCEPT.ppt

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