Data Mining: ApplicationsApplications and Trends in Data MiningData Mining ApplicationsBiomedical Data Mining and DNA AnalysisDNA Analysis: ExamplesData Mining for Financial Data AnalysisFinancial Data MiningData Mining for Retail IndustryData Mining in Retail Industry: ExamplesData Mining for Telecomm. Industry (1)Data Mining for Telecomm. Industry (2)Slide 12How to choose a data mining system?How to Choose a Data Mining System? (2)How to Choose a Data Mining System? (3)Examples of Data Mining Systems (1)Examples of Data Mining Systems (2)Slide 18Visual Data MiningVisual Data Mining & Data VisualizationData Mining Result VisualizationSAS Enterprise Miner: scatter plotsAssociation rules in MineSet 3.0Visualization of a decision tree in MineSet 3.0Cluster groupings in IBM Intelligent MinerData Mining Process VisualizationData Mining Processes by ClementineInteractive Visual Data MiningAudio Data MiningScientific and Statistical Data Mining (1)Scientific and Statistical Data Mining (2)Scientific and Statistical Data Mining (3)Theoretical Foundations of Data Mining (1)Theoretical Foundations of Data Mining (2)Data Mining and Intelligent Query AnsweringData Mining and Intelligent Query Answering (2)Slide 37Is Data Mining a Hype or Will It Be Persistent?Life Cycle of Technology AdoptionSocial Impacts: Threat to PrivacyProtect Privacy and Data SecuritySlide 42Trends in Data Mining (1)Trends in Data Mining (2)Slide 45SummaryReferences (1)References (2)References (3)Data Mining: ApplicationsDr. Hany SaleebApplications and Trends in Data MiningData mining applicationsData mining system products and research prototypesAdditional themes on data miningSocial impact of data miningTrends in data miningSummaryData Mining ApplicationsData mining is a young discipline with wide and diverse applicationsThere is still a nontrivial gap between general principles of data mining and domain-specific, effective data mining tools for particular applicationsSome application domains (covered in this chapter)Biomedical and DNA data analysisFinancial data analysisRetail industryTelecommunication industryBiomedical Data Mining and DNA AnalysisDNA sequences: 4 basic building blocks (nucleotides): adenine (A), cytosine (C), guanine (G), and thymine (T). Gene: a sequence of hundreds of individual nucleotides arranged in a particular orderHumans have around 100,000 genesTremendous number of ways that the nucleotides can be ordered and sequenced to form distinct genesSemantic integration of heterogeneous, distributed genome databasesCurrent: highly distributed, uncontrolled generation and use of a wide variety of DNA dataData cleaning and data integration methods developed in data mining will helpDNA Analysis: ExamplesSimilarity search and comparison among DNA sequencesCompare the frequently occurring patterns of each class (e.g., diseased and healthy)Identify gene sequence patterns that play roles in various diseases Association analysis: identification of co-occurring gene sequencesMost diseases are not triggered by a single gene but by a combination of genes acting togetherAssociation analysis may help determine the kinds of genes that are likely to co-occur together in target samplesPath analysis: linking genes to different disease development stagesDifferent genes may become active at different stages of the diseaseDevelop pharmaceutical interventions that target the different stages separatelyVisualization tools and genetic data analysisData Mining for Financial Data AnalysisFinancial data collected in banks and financial institutions are often relatively complete, reliable, and of high qualityDesign and construction of data warehouses for multidimensional data analysis and data miningView the debt and revenue changes by month, by region, by sector, and by other factorsAccess statistical information such as max, min, total, average, trend, etc.Loan payment prediction/consumer credit policy analysisfeature selection and attribute relevance rankingLoan payment performanceConsumer credit ratingFinancial Data MiningClassification and clustering of customers for targeted marketingmultidimensional segmentation by nearest-neighbor, classification, decision trees, etc. to identify customer groups or associate a new customer to an appropriate customer groupDetection of money laundering and other financial crimesintegration of from multiple DBs (e.g., bank transactions, federal/state crime history DBs)Tools: data visualization, linkage analysis, classification, clustering tools, outlier analysis, and sequential pattern analysis tools (find unusual access sequences)Data Mining for Retail IndustryRetail industry: huge amounts of data on sales, customer shopping history, etc.Applications of retail data mining Identify customer buying behaviorsDiscover customer shopping patterns and trendsImprove the quality of customer serviceAchieve better customer retention and satisfactionEnhance goods consumption ratiosDesign more effective goods transportation and distribution policiesData Mining in Retail Industry: ExamplesDesign and construction of data warehouses based on the benefits of data miningMultidimensional analysis of sales, customers, products, time, and regionAnalysis of the effectiveness of sales campaignsCustomer retention: Analysis of customer loyaltyUse customer loyalty card information to register sequences of purchases of particular customersUse sequential pattern mining to investigate changes in customer consumption or loyaltySuggest adjustments on the pricing and variety of goodsPurchase recommendation and cross-reference of itemsData Mining for Telecomm. Industry (1)A rapidly expanding and highly competitive industry and a great demand for data miningUnderstand the business involvedIdentify telecommunication patternsCatch fraudulent activitiesMake better use of resourcesImprove the quality of serviceMultidimensional analysis of telecommunication dataIntrinsically multidimensional: calling-time, duration, location of caller, location of callee, type of call, etc.Data Mining for Telecomm. Industry (2)Fraudulent pattern analysis and the identification of unusual patternsIdentify potentially fraudulent users and their atypical usage patternsDetect attempts to gain fraudulent entry to customer