Data mining. Theories, algorithms, and examples

Content: AN OVERVIEW OF DATA MINING METHODOLOGIESIntroduction to data mining methodologiesMETHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNSRegression modelsBayes classifiersDecision treesMulti-layer feedforward artificial neural networksSupport vector machinesSupervised clusteringMETHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNSHierarchical clusteringPartitional clusteringSelf-organized mapProbability distribution estimationAssociation rulesBayesian networksMETHODOLOGIES FOR MINING DATA REDUCTION PATTERNSPrincipal components analysisMulti-dimensional scalingLatent variable analysisMETHODOLOGIES FOR MINING OUTLIER AND ANOMALY PATTERNSUnivariate control chartsMultivariate control chartsMETHODOLOGIES FOR MINING SEQUENTIAL AND TIME SERIES PATTERNSAutocorrelation based time series analysisHidden Markov models for sequential pattern miningWavelet analysisHilbert transformNonlinear time series analysis