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Data Mining Concepts and Techniques Presentation Transcript
1.Data Mining: Concepts and Techniques
2.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: 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
3.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
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
4.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
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
5.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
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
6.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
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
7.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
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
8.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)
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)
9.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
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
10.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
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
11.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.
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.
12.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.
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.
13.Data Mining: A KDD Process
Data mining: the core of knowledge discovery process.
Data mining: the core of knowledge discovery process.
14.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
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
15.Data Mining and Business Intelligence
16.Architecture of a Typical Data Mining System
17.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
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
18.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”) à buys(X, “PC”) [support = 2%, confidence = 60%]
contains(T, “computer”) à contains(x, “software”) [1%, 75%]
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%]
19.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
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
20.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
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
21.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.
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.
22.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
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
23.Data Mining: Confluence of Multiple Disciplines
24.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
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
25.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.
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.
26.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
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
27.An OLAM Architecture
28.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
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
29.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
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
30.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
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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