Implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables.
|Author||Alexander Foss [aut, cre], Marianthi Markatou [aut]|
|Date of publication||2016-08-19 00:46:49|
|Maintainer||Alexander Foss <[email protected]>|
|Package repository||View on CRAN|
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