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فایل پی دی اف کامل پاورپوینت Tecniche di Data Mining PDF

اسلاید ۴: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione4فایل پی دی اف کامل پاورپوینت Tecniche di Data Mining PDFRiferimenti bibliografici Jiawei Han, Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2000 http:///books_catalog/catalog.aspISBN=1-55860-489-8 U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (editors). Advances in Knowledge discovery and data mining, MIT Press, 1996. David J. Hand, Heikki Mannila, Padhraic Smyth, Principles of Data Mining, MIT Press, 2001.S. Chakrabarti, Mining the Web: Discovering Knowledge from Hypertext Data, Morgan Kaufmann, ISBN 1-55860-754-4, 2002I lucidi utilizzati nelle lezioni saranno resi disponibili attraverso il sito web del corso: http://www-kdd.cnuce.cnr.it/

اسلاید ۵: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione5QuestionarioMessaggio e-mail con subject: Corso TDMContenutoNome e Cognome………………..e-mail:……………………………anno immatricolazione:…………….Corso di laurea :……………………..Corsi di basi di dati:· Frequentati nei precedenti semestri:· In questo semestre:

اسلاید ۶: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione6Contenuti del corso Introduction and Basic concepts (2 ore)Le applicazioniIl processo di knowledge discoveryData Consolidation & Data Preparation (4 +2 esercitazione)Nozioni basiche di Data Warehousing Nozioni basiche di analisi multidimensionale dei dati Regole Associative(6 +4 esercitazione)Regole intra-attributo, inter-attributo Calcolo efficiente di regole dassociazione: algoritmo Apriori e varianti Estensioni del concetto di regola dassociazione: tassonomie, regole quantitative, regole predittive. Regole associative e fattore Tempo: RdA Cicliche e Calendriche Pattern Sequenziali e Serie Temporali Basket Market Analysis utilizzando RdA

اسلاید ۷: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione7Contenuti del corso Classificazione con alberi di decisione (6 ore +2 esercitazione)Principali tecniche di classificazioneClassificatori bayesianiAlberi di decisione Rassegna di altri metodiApplicazione al rilevamento di frodi Clustering (2 ore +2 esercitazione)Principali tecniche di clusteringApplicazione al Customer segmentation Web Mining (4 ore)Temi avanzati (6 ore seminari)

اسلاید ۸: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione8Modalità di valutazioneEsercizi durante il corso (o Orale): 30% Seminario (o Progetto): 70% Students should pair up in teams. They will receive the same credit as their partner. Division of labor is up to them. Presentations should take 50 minutes, including 10 minutes for discussion. A presentation normally covers two or three closely related papers Transparencies should be made available to the rest of the class—preferably in PDF or HTML format.

اسلاید ۹: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione9Course OutlineIntroduction and basic conceptsMotivations, applications, the KDD process, the techniques Deeper into DM technologyAssociation Rules and Market Basket AnalysisDecision Trees and Fraud Detection Clustering and Customer SegmentationDeeper into Data PreparationBasic notion of DatawarehouseSelection and preprocessingAdvanced TopicsScalable DM algorithmsData mining query languagesMining on Web

اسلاید ۱۰: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione10Evolution of Database Technology: from data management to data analysis1960s: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 technology.

اسلاید ۱۱: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione11Motivations “Necessity is the Mother of Invention”Data explosion problem: Automated data collection tools, mature database technology and internet lead to tremendous amounts of data stored in databases, data warehouses and other information repositories. We are drowning in information, but starving for knowledge! (John Naisbett)Data warehousing and data mining :On-line analytical processingExtraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases.

اسلاید ۱۲: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione12Motivations for DM Abundance of business and industry dataCompetitive focus – Knowledge ManagementInexpensive, powerful computing enginesStrong theoretical/mathematical foundations machine learning & logicstatisticsdatabase management systems

اسلاید ۱۳: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione13Sources of DataBusiness Transactionswidespread use of bar codes => storage of millions of transactions daily (e.g., Walmart: 2000 stores => 20M transactions per day)most important problem: effective use of the data in a reasonable time frame for competitive decision-makinge-commerce dataScientific Datadata generated through multitude of experiments and observations examples, geological data, satellite imaging data, NASA earth observationsrate of data collection far exceeds the speed by which we analyze the dataFinancial Datacompany informationeconomic data (GNP, price indexes, etc.)stock markets

اسلاید ۱۴: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione14Sources of DataPersonal / Statistical Datagovernment censusmedical historiescustomer profilesdemographic datadata and statistics about sports and athletesWorld Wide Web and Online Repositoriesemail, news, messages Web documents, images, video, etc.link structure of of the hypertext from millions of Web sitesWeb usage data (from server logs, network traffic, and user registrations)online databases, and digital libraries

اسلاید ۱۵: Giannotti & Pedreschi Anno accademico, 2002/2003 Introduzione15Classes of applicationsDatabase analysis and decision support Market analysistarget marketing, customer relation management, market basket analysis, cross selling, market segmentation.Risk analysisForecasting, customer retention, improved underwriting, quality control, competitive analysis.Fraud detectionOther ApplicationsText (news group, email, documents) and Web analysis.Intelligent Query Answering

اسلاید ۱۶: Giannotti & Pedreschi Anno accademico, 2002/2003 IntroduzioneMarket AnalysisWhere are the data sources for analysisCredit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies.Target marketingFind clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.Determine customer purchasing patterns over timeConversion of single to a joint bank account: marriage, etc.Cross-market analysisAssociations/co-relations between product salesPrediction based on the association information.

اسلاید ۱۷: Giannotti & Pedreschi Anno accademico, 2002/2003 IntroduzioneCustomer profilingdata mining can tell you what types of customers buy what products (clustering or classification).Identifying customer requirementsidentifying the best products for different customersuse prediction to find what factors will attract new customersProvides summary informationvarious multidimensional summary reports;statistical summary information (data central tendency and variation)Market Analysis and ManagementMarket Analysis (2)

اسلاید ۱۸: Giannotti & Pedreschi Anno accademico, 2002/2003 IntroduzioneRisk AnalysisFinance planning and asset evaluation