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MIT-652: DM 1: Introduction to Data Mining 1
Introduction to Data Mining
MIT-652 Data Mining Applications
Thimaporn Phetkaew
School of Informatics,Walailak University
MIT-652: DM 1: Introduction to Data Mining 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
Data mining task primitives
Major issues in data mining
Summary
MIT-652: DM 1: Introduction to 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 (OLAP)
Data Mining: extraction of interesting knowledge (rules,
regularities, patterns, constraints) from data in large databases
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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: Data mining and data warehousing, multimedia databases, and Web databases
2000s: Data mining and its applications, Web technology (XML, data integration) and global information systems
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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
Data mining task primitives
Major issues in data mining
Summary
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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 databasesData mining: a misnomer?
Alternative names: Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.
Watch out: is everything “data mining”?Simple search and query processing Expert systems
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Potential Data Mining Applications
Database analysis and decision supportMarket analysis and management
Target marketing, customer relation management (CRM), market basket analysis, cross selling, market segmentation
Risk analysis and management
Forecasting, customer retention, improved underwriting, quality control, competitive analysis
Fraud detection and detection of unusual patterns (outliers)
Other ApplicationsText mining (news group, email, documents) and Web mining
Bioinformatics and bio-data analysis
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Data Mining: A KDD Process
Data mining: the core of knowledge discovery process
Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Data Selection
Data Mining
Pattern Evaluation
Data Transformation
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Steps of a KDD Process
Data cleaning: to remove noise and incomplete data
Data integration: where multiple data sources may be combined
Data selection: where data relevant to the analysis task are retrieved from the database/repository
Data transformation: where data are transformed into forms appropriate for mining
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Steps of a KDD Process
Data mining: where intelligent methods are applied in order to extract data patterns
Pattern evaluation: to identify the truly interesting patterns representing knowledge
Knowledge presentation: where visualization and knowledge representation techniques are used to present the mined knowledge to the user
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Data Mining and Business Intelligence
Increasing potentialto supportbusiness decisions
End User
BusinessAnalyst
DataAnalyst
DBA
DecisionsMaking
Data PresentationVisualization Techniques
Data MiningInformation Discovery
Data ExplorationStatistical Summary, Querying and Reporting
Data Preprocessing/Integration, Data Warehouses
Data SourcesPaper, Files, Web documents, Scientific Experiments, Database Systems
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Architecture of a Typical Data Mining System
data cleaning, integration, and selection
Database or Data Warehouse Server
Data Mining Engine
Pattern Evaluation
Graphical User Interface
Knowledge-Base
Database Data Warehouse
World-WideWeb
Other InfoRepositories
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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
Data mining task primitives
Major issues in data mining
Summary
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Data Mining: On What Kind of Data?
Relational databasesTransactional databasesData warehousesAdvanced DB and information repositories
Object-oriented and object-relational databasesSpatial databasesTime-series data and temporal dataText databases Multimedia databasesHeterogeneous and legacy databasesWWW
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Relational Databases
A collection of tables consisting of a set of tuples(records or rows)Each tuple owns a set of attributes (columns or fields) A tuple is identified by a unique keySQL “Show me total sales of last year grouped by branch”Data mining can search for trends and data patterns e.g. detect deviations such as
Items whose sales are far from those expected compared to last year; Predict credit risk of new customers based on their income, age,credit history
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Transactional Databases
Consist of a file where each record represents a transaction (TransID + ItemList, such as items purchased in a store)Q: How many transactions include item #13?Data mining: Market basket analysis: identify frequent itemsets to enable maximizing sales
Knowing printers are commonly purchased together with computers, thus discounting an expensive model printers with a purchase of selected computers (hoping selling more expensive printers)Shelf diapers, beers and potato chips nearby to increase sales
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Data Warehouses
A repository of information collected from multiple sources, organized under a unified schema at a single site in order to facilitate management decision makingProvide a multidimensional view of data and allows precomputation and fast accessing of summarized dataData warehouse systems are well suited for On-line Analytical Processing (OLAP)Data mining uses data warehouse tools to support data analysis
OLAP operations such as drill-down and roll-up
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Object-Oriented Databases
Based on OO programming paradigmData and code encapsulated into a single unitEntity is considered as object associated with
A set of variables describe the object (attributes in E-R model)A set of messages the object uses to communicate with other objectA set of methods which return a value in response upon receiving a message
Superclass/subclass with inheritance benefits information sharing
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Object-Relational Databases
Extend basic relational model by adding power to handle complex data types, class hierarchies, and object inheritanceData mining in OO and OR systems share some similarities with relational Data mining
Techniques need to be developed for handling complex object structure, class and subclass hierarchies, property inheritance,and methods and procedures
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Spatial Databases
Include geographic (map) databases, medical image databases and satellite image databasesApplications: forestry and ecology planning, vehicle navigation, providing public service information regarding location of telephone and electric cables, pipes, and sewage systemsData mining uncover patterns describing
Characteristics of houses located near a parkClimate of mountainous areas located at various altitudes
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Time-series and Temporal Databases
Time-series database stores sequences of values that change with time e.g. stock exchangeTemporal database stores relational data that include time-related attributesData mining finds characteristics of object evolution or the trend of changes for objects in database e.g.
Aid in scheduling of bank tellers according to the volume of customer trafficUncover trends that could help investment strategy planning (when is the best time to purchase XXX stock?)
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Text Databases
Contain word descriptions for objects such as library databases, articles, product specifications, and bug reportsData mining may uncover keyword or content associations, clustering behavior of text objects e.g. search engines cluster documents according to the words contained
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Multimedia Databases
Store text, image, audio,video dataApplication: picture content-based retrieval, voice mail system, speech-based user interfacesData mining needs storage and search techniques to be integrated to support real-time retrieval of countinuousmedia data
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Heterogeneous and Legacy Databases
Heterogenous database consists of a set of interconnected, autonomous component databasesLegacy database is a group of heterogeneous databases that combined different kinds of data systems, such as relational or OO databases, spreadsheets, or file systemsData mining may provide an interesting solution to the information exchange problem by transforming the given data into higher, more generalized, conceptual levels
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A huge, widely distributed information repository made available by internetData mining: Mining path traversal patterns: understand user access patterns
Help providing efficient access between highly correlated objects (improve system desigh)Lead to better marketing decisions e.g. placing advertisementsProviding better customer classification and behavior analysis
World Wide Web
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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
Data mining task primitives
Major issues in data mining
Summary
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Data Mining Functionalities
Concept description: Characterization and discrimination
Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions
Frequent patterns, association (correlation and causality)
Multi-dimensional vs. single-dimensional associationage(X, “20..29”) ^ income(X, “20..29K”) -> buys(X, “PC”) [support = 20%, confidence = 60%]contains(X, “computer”) -> contains(X, “software”) [10%, 75%]
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Data Mining Functionalities
Classification and PredictionFinding models (functions) that describe and distinguish classesor 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 analysisClass 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
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Data Mining Functionalities
Outlier analysisOutlier: 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 analysisTrend and deviation: regression analysis
Sequential pattern mining: e.g., digital camera large SD memory
Periodicity analysis
Similarity-based analysis
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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
Data mining task primitives
Major issues in data mining
Summary
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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
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Are All the “Discovered” Patterns Interesting?
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.
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Can We Find All and Only Interesting Patterns?
Find all the interesting patterns: CompletenessCan a data mining system find all the interesting patterns?
Heuristic vs. exhaustive search
Association vs. classification vs. clustering
Search for only interesting patterns: OptimizationCan 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
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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
Data mining task primitives
Major issues in data mining
Summary
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Data Mining: Confluence of Multiple Disciplines
Data Mining
Database Technology Statistics
OtherDisciplines
InformationScience
MachineLearning Visualization
Algorithm
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Data Mining: Classification Schemes
General functionalityDescriptive data mining characterize general properties of data
in database e.g. association rules
Predictive data mining perform inference on current data in
order to make predictions
Different views, different classificationsData view: kinds of databases to be mined
Knowledge view: kinds of knowledge to be discovered
Method view: kinds of techniques utilized
Application view: kinds of applications adapted
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Different views, different classifications (1)Databases to be mined
Relational, transactional, object-oriented, object-relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW, etc.
Knowledge to be minedCharacterization, discrimination, association, classification, clustering, trend, deviation and outlier analysis, etc.Multiple/integrated functions and mining at multiple levels
Data Mining: Classification Schemes
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Different views, different classifications (2)Techniques utilized
Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, neural network, etc.
Applications adaptedRetail, telecommunication, banking, fraud analysis, DNA mining, stock market analysis, Web mining, Weblog analysis, etc.
Data Mining: Classification Schemes
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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
Data mining task primitives
Major issues in data mining
Summary
MIT-652: DM 1: Introduction to Data Mining 40
What Defines a Data Mining Task ?
Task-relevant data
Type of knowledge to be mined
Background knowledge
Pattern interestingness measurements
Visualization of discovered patterns
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Task-Relevant Data
Database or data warehouse name
Database tables or data cubes
Condition for data selection
Relevant attributes or dimensions
Data grouping criteria
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Types of knowledge to be mined
Characterization
Discrimination
Association
Classification/prediction
Clustering
Outlier analysis
Other data mining tasks
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Types of knowledge to be mined: Metapatterns
Metapatterns: all discovered patterns must match
For example, metarule for association rules
P(X:customer,W) ^ Q(X,Y) ⇒ buys(X,Z)
age(X,”30..39”) ^ income(X,”40K..49K”) ⇒ buys(X,”VCR”)
[2.2%, 60%]
occupation(X,”student”) ^ age(X,”20..29”) ⇒
buys(X,”Computer”) [1.4%, 70%]
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Background Knowledge: Concept Hierarchies
Schema hierarchyE.g., street < city < province_or_state < country
Set-grouping hierarchyE.g., {20-39} = young, {40-59} = middle_aged, {60-89} = senior
Operation-derived hierarchyE.g., email address: [email protected]
login-name < department < university < countryRule-based hierarchy
low_profit_margin (X) <= price(X, P1) and cost (X, P2) and (P1 - P2) < $50
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Measurements of Pattern Interestingness
Simplicitye.g., (association) rule length, (decision) tree size
Certaintye.g., confidence, P(A|B) = n(A and B)/ n (B), classification reliability or accuracy, certainty factor, rule strength, rule quality, discriminating weight, etc.
Utilitypotential usefulness, e.g., support (association), noise threshold (description)
Noveltynot previously known, contribute new information, e.g., exception
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Visualization of Discovered Patterns
Different backgrounds/usages may require different forms of representation
E.g., rules, tables, crosstabs, pie/bar chart etc.
Concept hierarchy is also important Discovered knowledge might be more understandable when represented at high level of abstraction
Interactive drill up/down, pivoting, slicing and dicing provide different perspective to data
Different kinds of knowledge require different representation: association, classification, clustering, etc.
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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
Data mining task primitives
Major issues in data mining
Summary
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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
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
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Major Issues in Data Mining
Issues relating to the diversity of data typesHandling relational and complex types of dataMining information from heterogeneous databases and global information systems (WWW)
Performance and scalability
Efficiency and scalability of data mining algorithms
Parallel, distributed and incremental mining methods
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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
Data mining task primitives
Major issues in data mining
Summary
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Summary
Database technology: evolved from primitive file processing to the development of database management systems
Data mining: discovering interesting patterns from large amounts of data in a variety of information repositories
A KDD process: includes data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge presentation
Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc.
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Summary
Pattern interestingness: easily understood, valid (with some degree of certainty), useful, novel
Measures of pattern interestingness, either objective or subjective can be used to guide the discovery process
Data mining systems: can be classified according to the kinds of databases mined, the kinds of knowledge mined, the techniques used, or the applications adapted
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Summary
Five primitives for specification of a data mining task
task-relevant data (i.e., the data set to be mined)
kind of knowledge to be mined
background knowledge
interestingness measures
knowledge presentation and visualization techniques to be used for displaying the discovered patterns