Intro to Data Mining..

Worapoj Kreesuradej, Ph.D.Associate Professor

Data Mining & Data Exploration Laboratory (DME Lab),Faculty of Information Technology,King Mongkut's Institute of Technology Ladkrabang,Web: www.it.kmitl.ac.th/dmeEmail: worapoj@it.kimitl.ac.th, dme@it.kmitl.ac.th.

Grading z assignment 40 %z midterm exam 30 %z final exam 30 %

Referencesz Christopher Westphal and Teresa

Blaxton, Data Mining Solutions, John Wiley & Sons Inc., 1998.

z Pieter Adrians and Dolf Zantinge, Data Mining, Addison Wesley, 1996.

z Michael J.A. Berry and Gordon Linoff, Data Mining Techniques, John Wiley & Sons Inc., 1997.

z Alex Berson and Stephen J. Smith, Data Warehousing, Data Mining & OLAP, McGraw Hill, 1997.

Referencesz Vasant Dhar and Roger Stein, Seven

Methods for Transforming Corporate Data Into Business Intelligence, Prentice Hall, 1997.

z Cabena Peter etc., Discovering data mining, Prentice Hall, 1997.

z Business Modeling and Data Miningby Dorian Pyle , Morgan Kaufmann; 1st edition (April 2003) .

z Exploratory Data Mining and Data Cleaningby Tamraparni Dasu (Author), Theodore Johnson (Author) , John Wiley & Sons; 1st edition (May 9, 2003)

Referencesz Building Data Mining Applications for CRM by

Alex Berson, Kurt Thearling, Stephen J. Smith, McGraw-Hill Osborne Media; (December 22, 1999)

z Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Databy Bruce Ratner , : Chapman & Hall; (May 2003)

z Data Mining, Ian H. Witten, Eible Frank, Morgan Kaufman, 2005.

What is Data Mining? zDefinition: Data Mining is the

process of extracting previously unknown, valid and actionable information from large database and then using the information to make crucial business decisions.zAlternative names: Knowledge

Discovery in Databases (KDD)

Evolution of Database Technologyz 1960s: Data collection, database

creation, and network DBMSz 1970s: Relational data model,

relational DBMS implementation

Evolution of Database Technologyz 1980s: RDBMS, advanced data

models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.)z 1990s2000s: Data mining and

data warehousing, multimedia databases, and Web databases

CustomApplication

Intelligence Enterprise

ERP

CustomApplication

Packaged Application

Client Data

DataWarehouse

Data Mining

OLAP

Query & Reporting tool

Business ImpactIncreasing business Impact

Data Mining

Information Discovery

Data ExplorationOLAP

Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts

Data SourcesPaper, Files, Information Providers, Database Systems, OLTP

Potential Applications of Data MiningzMarket analysis and managementpurchasing pattern over timecross-sellingcustomer profilingdirect mail campaignmarket segmentation

Potential Applications of Data Mining

zRisk analysis and management forecastingcredit scoring for loan application

processingprofile of attrition (churn management)

Potential Applications of Data Miningz Fraud detection and managementmoney laundering detect suspicious

money transactionsdetecting Inappropriate Medical

Treatments

Potential Applications of Data MiningzWeb miningWeb Usage MiningWeb Content Mining Automatic Classification of Web

DocumentWeb Structure Mining

Potential Applications of Data Miningz Text mining Dividing documents into groups Document feature extraction

Structured Data

UnstructuredData

Data mining process

Data Preparation

Databases

Business Objective

Selection

Data Mining

Pattern Evaluation

PreprocessedData

TargetData

Data mining processzBusiness Objectives Determination Identify the business problems or

opportunityzData Selection Identify all internal or external sources

of information and select which subset of the data is needed for the data mining application.

Data mining processzData PreprocessingThe goal of data preprocessing is to

ensure the quality of the selected data.current data set, sampling data, unit

conversion, representation formats, detecting missing value

Data mining processzData Transformation the goal is to transform data to suit the

intended analysis and the data formats required by the data mining algorithms, many of which have particular requirements.

Data mining processzData Mining Select modeling techniqueData Mining Operations Predictive Modeling Database Segmentation Link Analysis Visualization

Database Segmentation (clustering)

z partitioning a database intosegments of similar records, that isrecords that share a number ofproperties.Model: K-means, Kohonen neural

networks

Database Segmentation

Age

Annual Income

Predictive Modeling

z Finding models for future predictionClassification:predicts categorical class labels

Prediction: models continuous-valued functions

zModel: decision-tree, neural network

Example of Predictive Modeling: Classification

Classifier

TrainingData Unseen Data

(Jeff, Professor, 4)

Tenured?NAME RANK YEARS TENUREDTom Assistant Prof 2 noMerlisa Associate Prof 7 noGeorge Professor 5 yesJoseph Assistant Prof 7 yes

Link Analysisz Finding frequent patterns,

associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositoriesModel: Apiori Algorithm,

Visualization of Link Analysis software

Visualization Visualization

Visualization of a decision tree in MineSet 3.0 Data mining process

zAnalysis of results Interpret and evaluate the output form

data mining.Have we found something that is

interesting, valid, and actionable?

Data mining processzAssimilation of knowledgeThe objective is to put into action,

according to the new, valid and actionable information from the previous process steps.

Effort Required for Each Data Mining Process Step

Methodology for data miningzCRISP-DMCross Industry Standard

Process for Data Mining (CRISP-DM)

zConsortium of data miners from various industries manufacturing, marketing, and government

Examples of Data Mining Systemsz IBM Intelligent MinerA wide range of data mining algorithmsScalable mining algorithmsToolkits: neural network algorithms,

statistical methods, data preparation, and data visualization toolsTight integration with IBM's DB2

relational database system

Examples of Data Mining Systems

zSAS Enterprise Miner A variety of statistical analysis toolsData warehouse tools and multiple data

mining algorithmszClementine (from SPSS)Multiple data mining algorithms and

advanced statistics

Examples of Data Mining SystemszSQL Server 2005Multiple data mining modules:

discovery-driven OLAP analysis, association, classification, and clustering Tight integration with SQL Server

relational database system

Examples of Data Mining SystemszOracle Data MinerMultiple data mining modules:

discovery-driven OLAP analysis, association, classification, and clustering

Examples of Data Mining SystemszWeka (Open Source Software)Multiple data mining modules:

association, classification, visualization and clustering Open Source Software

Examples of Data Mining SystemszDBMiner (DBMiner Technology

Inc.)Multiple data mining modules:

discovery-driven OLAP analysis, association, classification, and clustering Efficient, association and sequential-

pattern mining functions, and visual classification toolMining both relational databases and

data warehouses

Trends in Data MiningzApplication explorationdevelopment of application-specific

data mining system Invisible data mining (mining as built-in

function)

Trends in Data MiningzScalable data mining methodsConstraint-based mining: use of

constraints to guide data mining systems in their search for interesting patterns

z Integration of data mining with database systems, data warehouse systems, and Web database systems

Trends in Data MiningzStandardization of data mining

languageA standard will facilitate systematic

development, improve interoperability, and promote the education and use of data mining systems in industry and society PMML (Predictive Model Markup Language) OLE DB for Data Mining JDM API (Java Data Mining API)

zWeb MiningzMultimedia Mining

Data Mining: Confluence of Multiple Disciplines

Data Mining

Database Technology Statistics

OtherDisciplines

InformationScience

MachineLearning Visualization

Thank you !!!