Email this article 
Hide
Show all
| Authors: | Alexander H. Foss, Marianthi Markatou | ||||
| Title: | kamila: Clustering Mixed-Type Data in R and Hadoop | ||||
| Abstract: | In this paper we discuss the challenge of equitably combining continuous (quantitative) and categorical (qualitative) variables for the purpose of cluster analysis. Existing techniques require strong parametric assumptions, or difficult-to-specify tuning parameters. We describe the kamila package, which includes a weighted k-means approach to clustering mixed-type data, a method for estimating weights for mixed-type data (ModhaSpangler weighting), and an additional semiparametric method recently proposed in the literature (KAMILA). We include a discussion of strategies for estimating the number of clusters in the data, and describe the implementation of one such method in the current R package. Background and usage of these clustering methods are presented. We then show how the KAMILA algorithm can be adapted to a map-reduce framework, and implement the resulting algorithm using Hadoop for clustering very large mixed-type data sets. | ||||
|
Page views:: 5179. Submitted: 2016-08-28. Published: 2018-02-27. |
|||||
| Paper: |
kamila: Clustering Mixed-Type Data in R and Hadoop
Download PDF
(Downloads: 4821)
|
||||
| Supplements: |
| ||||
| DOI: |
10.18637/jss.v083.i13
|
This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license. |