Download PyMathCamp syllabus Topic: Data Mining Chapter 1: Introduction 1

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PyMathCampsyllabus
Topic:DataMining
Chapter1:Introduction
1. Motivation:Whydatamining?
2. Whatisdatamining?
3. DataMining:Onwhatkindofdata?
4. Dataminingfunctionality
5. Classificationofdataminingsystems
6. Top-10mostpopulardataminingalgorithms
7. Majorissuesindatamining
Chapter2:DataPreprocessing
1. Whypreprocessthedata?
2. Descriptivedatasummarization
3. Datacleaning
4. Dataintegrationandtransformation
5. Datareduction
6. Discretizationandconcepthierarchygeneration
Chapter3:DataWarehouseandOLAPTechnology:AnIntroduction
1. Whatisadatawarehouse?
2. Amulti-dimensionaldatamodel
3. Datawarehousearchitecture
4. Datawarehouseimplementation
5. Fromdatawarehousingtodatamining
Chapter4:AdvancedDataCubeTechnologyandDataGeneralization
1. EfficientComputationofDataCubes
2. ExplorationandDiscoveryinMultidimensionalDatabases
3. Attribute-OrientedInduction─AnAlternativeDataGeneralizationMethod
Chapter5:MiningFrequentPatterns,AssociationandCorrelations
1. Basicconceptsandaroadmap
2. Efficientandscalablefrequentitemsetminingmethods
3. Miningvariouskindsofassociationrules
4. Fromassociationminingtocorrelationanalysis
5. Constraint-basedassociationmining
Chapter6:ClassificationandPrediction
1. Whatisclassification?Whatisprediction?
2. Issuesregardingclassificationandprediction
3. Classificationbydecisiontreeinduction
4. Bayesianclassification
5. Rule-basedclassification
6. Classificationbybackpropagation
7. SupportVectorMachines(SVM)
8. Associativeclassification
9. Lazylearners(orlearningfromyourneighbors)
10. Otherclassificationmethods
11. PredictionAccuracyanderrormeasures
12. Ensemblemethods
13. Modelselection
Chapter7:ClusterAnalysis
1. WhatisClusterAnalysis?
2. TypesofDatainClusterAnalysis
3. ACategorizationofMajorClusteringMethods
4. PartitioningMethods
5. HierarchicalMethods
6. Density-BasedMethods
7. Grid-BasedMethods
8. Model-BasedMethods
9. ClusteringHigh-DimensionalData
10. Constraint-BasedClustering
11. OutlierAnalysis
Chapter8:Miningdatastreams,time-series,andsequencedata
1. Miningdatastreams
2. Miningtime-seriesdata
3. Miningsequencepatternsintransactionaldatabases
4. Miningsequencepatternsinbiologicaldata
Chapter9:Mininggraphs,socialnetworksandmulti-relationaldata
1. Graphmining
2. Socialnetworkanalysis
3. Multi-relationaldatamining
Chapter10:Miningobject,spatial,multimedia,textandWebdata(Miningcomplexdata
objects,Spatialandspatiotemporaldatamining,Multimediadatamining,Textminingand
Webmining)
1. Miningobjectdata
2. Spatialandspatiotemporaldatamining
3. Multimediadatamining
4. Textmining
5. Webmining
Chapter11:Applicationsandtrendsofdatamining(Miningbusiness&biologicaldata,
VisualdataminingandDataminingandsociety:Privacy-preservingdatamining)
1. Dataminingapplications
2. Dataminingproductsandresearchprototypes
3. Additionalthemesondatamining
4. Socialimpactsofdatamining
5. Trendsindatamining
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