Data Mining: Application and trends in data mining

Data Mining: Application and trends in data mining

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  Application and TrendsofData Mining
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  Data Mining for FinancialData AnalysisDesign and construction of data warehouses for multidimensional data analysis and data miningLoan payment prediction and customer credit policy analysisClassification and clustering of customers for targeted marketingDetection of money laundering and other financial crimesData Mining for the Retail Industry
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  A few examplesof data mining in the retail industryDesign and construction of data warehouses based on the benefits of data miningMultidimensional analysis of sales, customers, products, time, and regionAnalysis of the effectiveness of sales campaignsCustomer retention—analysis of customer loyaltyProduct recommendation and cross-referencing of items
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  Data Mining forthe Telecommunication IndustryMultidimensional analysis of telecommunication dataFraudulent pattern analysis and the identification of unusual patternsMultidimensional association and sequential pattern analysis:Mobile telecommunication servicesUse of visualization tools in telecommunication data analysis
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  Data Mining for BiologicalData AnalysisSemantic integration of heterogeneous, distributed genomic and proteomic databases.Alignment, indexing, similarity search, and comparative analysis of multiple nucleotide , protein sequences.Discovery of structural patterns and analysis of genetic networks and protein pathways.Association and path analysis: identifying co-occurring gene sequences and linking genes to different stages of disease development.Visualization tools in genetic data analysis.
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  Data Mining in ScientificApplicationsScientific data can be amassed at much higher speeds and lower costs. This has resulted in the accumulation of huge volumes of high-dimensional data, stream data, and heterogeneous data, containing rich spatial and temporal information.Scientific applications are shifting from the “hypothesize-and-test” paradigm toward a “collect and store data, mine for new hypotheses, confirm with data or experimentation” process.
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  Data Mining for IntrusionDetectionDevelopment of data mining algorithms for intrusion detectionAssociation and correlation analysis, and aggregation to help select and build discriminating attributesAnalysis of stream dataDistributed data miningVisualization and querying tools
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  Trends in DataMining Application explorationScalable and interactive data mining methodsIntegration of data mining with database systems, data warehouse systems, and Webdatabase systemsStandardization of data mining languageVisual data mining
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  Cont..Biological data miningDatamining and software engineeringWeb miningDistributed data miningReal-time or time-critical data miningGraph mining, link analysis, and social network analysis
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  Cont..Multi relational andmulti database data miningNew methods for mining complex types of dataPrivacy protection and information security in data mining
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  Assessment of aData mining SystemMust be based on: 1. Data types2. System issues3. Data sources4. Data mining functions and methodologies.5. Coupling data mining with database and/or data warehouse systems.6. Scalability7. Visualization tools8. Data mining query language and graphical user interface
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  Theoretical Foundations ofData MiningData reductionData compressionPattern discoveryProbability theoryMicroeconomic viewInductive databases
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  Statistical Data Miningtechniques 1. Regression2. Generalized linear model3. Analysis of variance4. mixed effect model5. Factor analysis6. Discriminate analysis7. Time series analysis8. Survival analysis9. Quality control
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  Visual and AudioData MiningVisual data mining discovers implicit and useful knowledge from large data sets using data and/or knowledge visualization Data visualization and data mining can be integrated in the following ways: Data visualizationData mining result visualizationData mining process visualizationInteractive visual data mining techniques.
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  Security of DataMiningData security enhancing techniques have been developed to help protect data. Databases can employ a multilevel security model to classify and restrict data according to various security levels, with users permitted access to only their authorized level. Privacy-sensitive data mining deals with obtaining valid data mining results without learning the underlying data values.
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