Data Mining and Data Warehousing

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Unit-I Overview, Motivation(for Data Mining),Data Mining-Definition & Functionalities, Data Processing, Form of Data Preprocessing, Data Cleaning: Missing Values, ois! Data,("inning, Clustering, #egression, Computer an$ %uman inspection),&nconsistent Data, Data &ntegration an$ 'ransformation( Data Reduction:-Data Cu)e *ggregation, Dimensionalit! re$uction, Data Compression, umerosit! #e$uction, Clustering, Discreti+ation an$ Concept ,ierarc,! generation( Unit-II Conce t De!c"i tion:- Definition, Data -enerali+ation, *nal!tical C,aracteri+ation, *nal!sis of attri)ute relevance, Mining Class comparisions, .tatistical measures in large Data)ases( Measuring Central 'en$enc!, Measuring Dispersion of Data, -rap, Displa!s of "asic .tatistical class Description, Mining *ssociation #ules in /arge Data)ases, *ssociation rule mining, mining .ingle-Dimensional "oolean *ssociation rules from 'ransactional Data)ases0 *priori *lgorit,m, Mining Multilevel *ssociation rules from 'ransaction Data)ases an$ Mining Multi-Dimensional *ssociation rules from #elational Data)ases Unit-III C#a!!i$ication and %"ediction!: 1,at is Classification & Pre$iction, &ssues regar$ing Classification an$ pre$iction, Decision tree, "a!esian Classification, Classification )! "ac2 propagation, Multila!er fee$-forwar$ eural etwor2, "ac2 propagation *lgorit,m, Classification met,o$s 3nearest neig,)or classifiers, -enetic *lgorit,m( C#u!te" Ana#&!i!: Data t!pes in cluster anal!sis, Categories of clustering met,o$s, Partitioning met,o$s( %ierarc,ical Clustering- C4#5 an$ C,ameleon( Densit! "ase$ Met,o$s-D".C* , OP'&C.( -ri$ "ase$ Met,o$s- .'& -, C/&645( Mo$el "ase$ Met,o$ 0.tatistical *pproac,, eural etwor2 approac,, Outlier *nal!sis Unit-I' Data Wa"e(ou!in): Overview, Definition, Deliver! Process, Difference )etween Data)ase .!stem an$ Data 1are,ouse, Multi Dimensional Data Mo$el, Data Cu)es, .tars, .now Fla2es, Fact Constellations, Concept ,ierarc,!, Process *rc,itecture, 7 'ier *rc,itecture, Data Marting( Unit-' *ggregation, %istorical information, 6uer! Facilit!, O/*P function an$ 'ools( O/*P .ervers, #O/*P, MO/*P, %O/*P, Data Mining interface, .ecurit!, "ac2up an$ #ecover!, 'uning Data 1are,ouse, 'esting Data 1are,ouse( *oo+!, 8( M(%(Dun,am,9Data Mining:&ntro$uctor! an$ *$vance$ 'opics9 Pearson 5$ucation :( ;iawei %an, Mic,eline 3am)er, 9Data Mining Concepts & 'ec,ni<ues9 5lsevier 7( .am *na,or!, Dennis Murra!, =Data 1are,ousing in t,e #eal 1orl$: * Practical -ui$e for "uil$ing Decision .upport .!stems, 8>e =Pearson 5$ucation ?( Mallac,,9Data 1are,ousing .!stem9,Mc-raw 0%ill

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