The main function kcca implements a general framework for k-centroids cluster analysis supporting arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability.
|Author||Friedrich Leisch [aut, cre], Evgenia Dimitriadou [ctb]|
|Date of publication||2018-02-14 13:38:47 UTC|
|Maintainer||Friedrich Leisch <[email protected]>|
|Package repository||View on CRAN|
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