Applied Unsupervised Learning with R

Startingwiththebasics,AppliedUnsupervisedLearningwithRexplainsclusteringmethods,distributionanalysis,dataencoders,andfeaturesofRthatenableyoutounderstandyourdatabetterandgetanswerstoyourmostpressingbusinessquestions.Thisbookbeginswiththemostimportantandcommonlyusedmethodforunsupervisedlearning-clustering-andexplainsthethreemainclusteringalgorithms-k-means,divisive,andagglomerative.Followingthis,you'llstudymarketbasketanalysis,kerneldensityestimation,principalcomponentanalysis,andanomalydetection.You'llbeintroducedtothesemethodsusingcodewritteninR,withfurtherinstructionsonhowtoworkwith,edit,andimproveRcode.Tohelpyougainapracticalunderstanding,thebookalsofeaturesusefultipsonapplyingthesemethodstorealbusinessproblems,includingmarketsegmentationandfrauddetection.Byworkingthroughinterestingactivities,you'llexploredataencodersandlatentvariablemodels.Bytheendofthisbook,youwillhaveabetterunderstandingofdifferentanomalydetectionmethods,suchasoutlierdetection,Mahalanobisdistances,andcontextualandcollectiveanomalydetection.