05 Dbdm2007 Data Mining - Leiden University
Data Mining:Concepts and Techniques— Chapter 5 —Jiawei HanDepartment of Computer ScienceUniversity of Illinois at Urbana-Champaignwww.cs.uiuc.edu/ hanj 2006 Jiawei Han and Micheline Kamber, All rights reservedOctober 22, 2007Data Mining: Concepts and Techniques1October 22, 2007Why Data Mining? Data collection and data availability Data mining (knowledge discovery from data) Major sources of abundant data Business: Web, e-commerce, transactions, stocks, Science: Remote sensing, bioinformatics, scientific simulation, Society and everyone: news, digital cameras, We are drowning in data, but starving for knowledge! “Necessity is the mother of invention”—Data mining—Automated TechnologyData Mining: Concepts and Techniques3Data mining: a misnomer?Alternative names analysis of massive data sets: natural from the evolution of DatabaseOctober 22, 2007Extraction of interesting (non-trivial, implicit, previouslyunknown and potentially useful) patterns or knowledge fromhuge amount of dataAutomated data collection tools, database systems, Web,computerized society 2What Is Data Mining?The Explosive Growth of Data: from terabytes to petabytes Data Mining: Concepts and TechniquesKnowledge discovery (mining) in databases (KDD), knowledgeextraction, data/pattern analysis, data archeology, datadredging, information harvesting, business intelligence, etc.Watch out: Is everything “data mining”? Simple search and query processing (Deductive) expert systemsOctober 22, 2007Data Mining: Concepts and Techniques41
Why Data Mining?—Potential Applications Data analysis and decision support Target marketing, customer relationship management (CRM),market basket analysis, cross selling, market segmentationData mining—core ofknowledge discoveryprocessTask-relevant DataForecasting, customer retention, improved underwriting,quality control, competitive analysisData CleaningData IntegrationText mining (news group, email, documents) and WebminingOctober 22, 2007Std tData Mining: Concepts and TechniquesDatabases5i iData Mining and Business IntelligenceIncreasing potentialto supportbusiness decisionsDecisionMakingData PresentationVisualization TechniquesOctober 22, ngDataAnalystData ExplorationStatistical Summary, Querying, and ReportingPatternRecognitionData Preprocessing/Integration, Data WarehousesData SourcesPaper, Files, Web documents, Scientific experiments, Database SystemsData Mining: Concepts and TechniquesData Mining: Concepts and Techniques6Data Mining: Confluence of Multiple DisciplinesEnd UserData MiningInformation DiscoveryOctober 22, 2007SelectionData WarehouseFraud detection and detection of unusual patterns(outliers)Other Applications Pattern EvaluationData MiningRisk analysis and management Market analysis and management Knowledge Discovery (KDD) ProcessStatisticsData tober 22, 2007Data Mining: Concepts and Techniques82
Why Not Traditional Data Analysis? Tremendous amount of data Algorithms must be highly scalable to handle such as tera-bytes ofdataData to be mined High-dimensionality of data Multi-Dimensional View of Data Mining Micro-array may have tens of thousands of dimensionsKnowledge to be mined High complexity of data Data streams and sensor data Time-series data, temporal data, sequence data Structure data, graphs, social networks and multi-linked data Heterogeneous databases and legacy databases Spatial, spatiotemporal, multimedia, text and Web data Software programs, scientific simulations Data Mining: Concepts and Techniques 9 Descriptive data mining Predictive data miningData view: Kinds of data to be mined Knowledge view: Kinds of knowledge to be discovered Method view: Kinds of techniques utilized Application view: Kinds of applications adaptedOctober 22, 2007Data Mining: Concepts and TechniquesData Mining: Concepts and Techniques Database-oriented data sets and applications Advanced data sets and advanced applications Different views lead to different classifications Retail, telecommunication, banking, fraud analysis, bio-data mining, stockmarket analysis, text mining, Web mining, etc.10Data Mining: On What Kinds of Data?General functionality Database-oriented, data warehouse (OLAP), machine learning, statistics,visualization, etc.October 22, 2007Data Mining: Classification Schemes Multiple/integrated functions and mining at multiple levelsApplications adaptedNew and sophisticated applicationsOctober 22, 2007Characterization, discrimination, association, classification, clustering,trend/deviation, outlier analysis, etc.Techniques utilized Relational, data warehouse, transactional, stream, objectoriented/relational, active, spatial, time-series, text, multi-media,heterogeneous, legacy, WWW11Relational database, data warehouse, transactional database Data streams and sensor data Time-series data, temporal data, sequence data (incl. bio-sequences) Structure data, graphs, social networks and multi-linked data Object-relational databases Heterogeneous databases and legacy databases Spatial data and spatiotemporal data Multimedia database Text databases The World-Wide WebOctober 22, 2007Data Mining: Concepts and Techniques123
Data Mining Functionalities Data Mining Functionalities (2)Multidimensional concept description: Characterization and discrimination Frequent patterns, association, correlation vs. causality Classification and prediction Cluster analysis Diaper Æ Beer [0.5%, 75%] (Correlation or causality?) Predict some unknown or missing numerical values October 22, 2007Data Mining: Concepts and Techniques13October 22, 2007 interesting A pattern is interesting if it is easily understood by humans, valid on newor 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. 14Find all the interesting patterns: CompletenessSuggested approach: Human-centered, query-based, focused miningInterestingness measures Data Mining: Concepts and TechniquesFind All and Only Interesting Patterns?Data mining may generate thousands of patterns: Not all of them are Trend and deviation: e.g., regression analysisSequential pattern mining: e.g., digital camera Æ large SD memoryPeriodicity analysisSimilarity-based analysisOther pattern-directed or statistical analysesAre All the “Discovered” Patterns Interesting? Outlier: Data object that does not comply with the general behaviorof the dataNoise or exception? Useful in fraud detection, rare events analysisTrend and evolution analysis E.g., classify countries based on (climate), or classify cars based on(gas mileage)Class label is unknown: Group data to form new classes, e.g.,cluster houses to find distribution patternsMaximizing intra-class similarity & minimizing interclass similarityOutlier analysis Construct models (functions) that describe and distinguish classesor concepts for future prediction Generalize, summarize, and contrast data characteristics, e.g., dryvs. wet regionsSubjective: based on user’s belief in the data, e.g., unexpectedness,Can a data mining system find all the interesting patterns? Do weneed to find all of the interesting patterns? Heuristic vs. exhaustive search Association vs. classification vs. clusteringSearch for only interesting patterns: An optimization problem 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 optimizationnovelty, actionability, etc.October 22, 2007Data Mining: Concepts and Techniques15October 22, 2007Data Mining: Concepts and Techniques164
Why Data Mining Query Language?Other Pattern Mining Issues Precise patterns vs. approximate patterns But approximate patterns can be more compact and sufficient How to find high quality approximate patterns? How to derive efficient approximate pattern mining algorithms? Why constraint-based mining?What are the possible kinds of constraints? How to pushconstraints into the mining process?October 22, 2007Data Mining: Concepts and Techniques17Finding all the patterns autonomously in a database?—unrealisticbecause the patterns could be too many but uninterestingData mining should be an interactive process Constrained vs. non-constrained patterns Automated vs. query-driven? Gene sequence mining: approximate patterns are inherent Association and correlation mining: possible find sets of precisepatternsUser directs what to be minedUsers must be provided with a set of primitives to be used to communicatewith the data mining systemIncorporating these primitives in a data mining query language More flexible user interaction Foundation for design of graphical user interface Standardization of data mining industry and practiceOctober 22, 2007Primitives that Define a Data Mining TaskData Mining: Concepts and TechniquesPrimitive 1: Task-Relevant Data Task-relevant data Database or data warehouse name Type of knowledge to be mined Database tables or data warehouse cubes Background knowledge Condition for data selection Pattern interestingness measurements Relevant attributes or dimensions Visualization/presentation of discovered patterns Data grouping criteriaOctober 22, 2007Data Mining: Concepts and Techniques1819October 22, 2007Data Mining: Concepts and Techniques205
Primitive 3: Background KnowledgePrimitive 2: Types of Knowledge to Be Mined Characterization A typical kind of background knowledge: Concept hierarchies Discrimination Schema hierarchy Association Set-grouping hierarchy Classification/prediction Clustering Outlier analysis Other data mining tasks login-name department university country21Simplicity Interactive drill up/down, pivoting, slicing and dicing providedifferent perspectives to data NoveltyDifferent kinds of knowledge require different representation: association,classification, clustering, etc.not previously known, surprising (used to remove redundantrules, e.g., Illinois vs. Champaign rule implication support ratio)Data Mining: Concepts and TechniquesDiscovered knowledge might be more understandable whenrepresented at high level of abstractionUtilityOctober 22, 2007E.g., rules, tables, crosstabs, pie/bar chart, etc.Concept hierarchy is also important potential usefulness, e.g., support (association), noise threshold(description) 22Different backgrounds/usages may require different forms of representation Certaintye.g., confidence, P(A B) #(A and B)/ #(B), classificationreliability or accuracy, certainty factor, rule strength, rule quality,discriminating weight, etc. Data Mining: Concepts and TechniquesPrimitive 5: Presentation of Discovered Patternse.g., (association) rule length, (decision) tree size low profit margin (X) price(X, P1) and cost (X, P2) and (P1 P2) 50October 22, 2007Primitive 4: Pattern Interestingness Measure email address: hagonzal@cs.uiuc.eduRule-based hierarchy Data Mining: Concepts and TechniquesE.g., {20-39} young, {40-59} middle agedOperation-derived hierarchy October 22, 2007E.g., street city province or state country23October 22, 2007Data Mining: Concepts and Techniques246
DMQL—A Data Mining Query LanguageAn Example Query in DMQLMotivation A DMQL can provide the ability to support ad-hoc andinteractive data miningBy providing a standardized language like SQLHope to achieve a similar effect like that SQL has on relationaldatabase Foundation for system development and evolution Facilitate information exchange, technology transfer,commercialization and wide acceptance Design DMQL is designed with the primitives described earlierOctober 22, 2007Data Mining: Concepts and Techniques25October 22, 2007Other Data Mining Languages &Standardization Efforts MSQL (Imielinski & Virmani’99) MineRule (Meo Psaila and Ceri’96) Query flocks based on Datalog syntax (Tsur et al’98) Based on OLE, OLE DB, OLE DB for OLAP, C# Integrating DBMS, data warehouse and data miningData mining systems, DBMS, Data warehouse systems coupling Providing a platform and process structure for effective data mining Emphasizing on deploying data mining technology to solve business Integration of multiple mining functions Data Mining: Concepts and TechniquesNecessity of mining knowledge and patterns at different levels ofabstraction by drilling/rolling, pivoting, slicing/dicing, etc.problemsOctober 22, 2007integration of mining and OLAP technologiesInteractive mining multi-level knowledgeDMML (Data Mining Mark-up Language) by DMG (www.dmg.org) No coupling, loose-coupling, semi-tight-coupling, tight-couplingOn-line analytical mining data OLEDB for DM (Microsoft’2000) and recently DMX (Microsoft SQLServer 2005) 26Integration of Data Mining and Data WarehousingAssociation rule language specifications Data Mining: Concepts and Techniques27Characterized classification, first clustering and then associationOctober 22, 2007Data Mining: Concepts and Techniques287
Coupling Data Mining with DB/DW Systems No coupling—flat file processing, not recommended Loose coupling Graphical User InterfaceFetching data from DB/DWPattern EvaluationSemi-tight coupling—enhanced DM performance Architecture: Typical Data Mining SystemProvide efficient implement a few data mining primitives in aDB/DW system, e.g., sorting, indexing, aggregation, histogramanalysis, multiway join, precomputation of some stat functionsData Mining EngineDatabase or DataWarehouse ServerTight coupling—A uniform information processing environment DM is smoothly integrated into a DB/DW system, mining queryis optimized based on mining query, indexing, query processingmethods, etc.data cleaning, integration, and selectionDatabaseOctober 22, 2007Data Mining: Concepts and Techniques29October 22, 2007Major Issues in Data Mining Performance: efficiency, effectiveness, and scalability Pattern evaluation: the interestingness problem Incorporation of background knowledge Handling noise and incomplete data Mining different kinds of knowledge from diverse data types, e.g., bio,stream, Web Parallel, distributed and incremental mining methodsIntegration of the discovered knowledge with existing one: knowledgefusion User interaction Data mining query languages and ad-hoc mining Expression and visualization of data mining resultsInteractive mining of knowledge at multiple levels of abstraction Applications and social impactsdata mining& invisibleminingOctober 22, Domain-specific2007Data Mining:Concepts anddataTechniquesDataWorld-Wide Other InfoRepositoriesWarehouseWebData Mining: Concepts and Techniques30SummaryMining methodology KnowledgeBase 31Data mining: Discovering interesting patterns from large amounts ofdataA natural evolution of database technology, in great demand, withwide applicationsA KDD process includes data cleaning, data integration, dataselection, transformation, data mining, pattern evaluation, andknowledge presentationMining can be performed in a variety of information repositoriesData mining functionalities: characterization, discrimination,association, classification, clustering, outlier and trend analysis, etc. Data mining systems and architectures Major issues in data miningOctober 22, 2007Data Mining: Concepts and Techniques328
Conferences and Journals on Data MiningA Brief History of Data Mining Society 1989 IJCAI Workshop on Knowledge Discovery in Databases Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley,1991)Advances in Knowledge Discovery and Data Mining (U. Fayyad, G.Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996) 1995-1998 International Conferences on Knowledge Discovery in Databases and DataMining (KDD’95-98) ACM SIGKDD conferences since 1998 and SIGKDD Explorations More conferences on data mining Journal of Data Mining and Knowledge Discovery (1997) 1991-1994 Workshops on Knowledge Discovery in Databases KDD Conferences PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM(2001), etc. ACM Transactions on KDD starting in 2007October 22, 2007Data Mining: Concepts and Techniques33 Conferences: SIGIR, WWW, CIKM, etc.Journals: WWW: Internet and Web Information Systems,Conference proceedings: CHI, ACM-SIGGraph, etc.Journals: IEEE Trans. visualization and computer graphics, etc.Data Mining: Concepts and Techniques(IEEE) ICDE WWW, SIGIR ICML, CVPR, NIPSJournals Data Mining and KnowledgeDiscovery (DAMI or DMKD)IEEE Trans. On Knowledgeand Data Eng. (TKDE) KDD Explorations ACM Trans. on KDDData Mining: Concepts and Techniques34S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000 T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003 U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining.U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd ed., 2006 D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001 T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference,and Prediction, Springer-Verlag, 2001Conferences: Joint Stat. Meeting, etc.Journals: Annals of statistics, etc.October 22, 2007VLDB Kaufmann, 2001Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc.Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems,IEEE-PAMI, etc.Visualization ACM SIGMOD AAAI/MIT Press, 1996Statistics Web and IR Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAAJournals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc.AI & Machine Learning Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDDDatabase systems (SIGMOD: ACM SIGMOD Anthology—CD ROM) Other related conferencesRecommended Reference BooksData mining and KDD (SIGKDD: CDROM) ACM SIGKDD Int. Conf. onKnowledge Discovery inDatabases and Data Mining(KDD)SIAM Data Mining Conf. (SDM)(IEEE) Int. Conf. on DataMining (ICDM)Conf. on Principles andpractices of KnowledgeDiscovery and Data Mining(PKDD)Pacific-Asia Conf. onKnowledge Discovery and DataMining (PAKDD)October 22, 2007Where to Find References? DBLP, CiteSeer, Google T. M. Mitchell, Machine Learning, McGraw Hill, 1997 G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991 P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998 I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with JavaImplementations, Morgan Kaufmann, 2nd ed. 200535October 22, 2007Data Mining: Concepts and Techniques369
Chapter 5: Mining Frequent Patterns,Association and Correlations Basic concepts and a road map Efficient and scalable frequent itemset miningmethods Mining various kinds of association rules From association mining to correlationanalysis Constraint-based association mining SummaryOctober 22, 2007Data Mining: Concepts and Techniques37October 22, 2007What Is Frequent Pattern Analysis? Frequent pattern: a pattern (a set of items, subsequences, substructures,First proposed by Agrawal, Imielinski, and Swami [AIS93] in the context Discloses an intrinsic and important property of data sets Forms the foundation for many essential data mining tasksof frequent itemsets and association rule mining Association, correlation, and causality analysisMotivation: Findin
Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization. 3 October 22, 2007 Data Mining: Concepts and Techniques 9 Why Not Traditional Data Analysis?
October 20, 2009 Data Mining: Concepts and Techniques 7 Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization October 20, 2009 Data Mining: Concepts and Techniques 8 Why Not Traditional Data Analysis? Tremendous amount of data
Preface to the First Edition xv 1 DATA-MINING CONCEPTS 1 1.1 Introduction 1 1.2 Data-Mining Roots 4 1.3 Data-Mining Process 6 1.4 Large Data Sets 9 1.5 Data Warehouses for Data Mining 14 1.6 Business Aspects of Data Mining: Why a Data-Mining Project Fails 17 1.7 Organization of This Book 21 1.8 Review Questions and Problems 23
Frans van Mieris was born in Leiden on 16 April 1635 to the Remonstrant goldsmith and diamond-cutter Jan Bastiaens van Mieris and his second wife, Christina van Garbartijn. 1 Frans came from a family of gold-and silversmiths; two uncles practiced this craft, as did his older cousin Willem Fransz van Mieris (ca. 1600-56).
DATA MINING What is data mining? [Fayyad 1996]: "Data mining is the application of specific algorithms for extracting patterns from data". [Han&Kamber 2006]: "data mining refers to extracting or mining knowledge from large amounts of data". [Zaki and Meira 2014]: "Data mining comprises the core algorithms that enable one to gain fundamental in
Data Mining and its Techniques, Classification of Data Mining Objective of MRD, MRDM approaches, Applications of MRDM Keywords Data Mining, Multi-Relational Data mining, Inductive logic programming, Selection graph, Tuple ID propagation 1. INTRODUCTION The main objective of the data mining techniques is to extract .
enable mining to leave behind only clean water, rehabilitated landscapes, and healthy ecosystems. Its objective is to improve the mining sector's environmental performance, promote innovation in mining, and position Canada's mining sector as the global leader in green mining technologies and practices. Source: Green Mining Initiative (2013).
Data Mining CS102 Data Mining Looking for patterns in data Similar to unsupervised machine learning Popularity predates popularity of machine learning "Data mining" often associated with specific data types and patterns We will focus on "market-basket" data Widely applicable (despite the name) And two types of data mining patterns
The level of management responsible for developing strategic goals is: A. line B. supervisor C. functional D. senior . D. other organisations that do business with the 14 Where a stakeholder is identified as having high interest and low power, an organisation should: keep them satisfied monitor their interests and power C. manage them closely keep them informed 15. Highfield Aeet 12 .
