Adapting k-means for supervised clustering

Al-Harbi, SH and Rayward-Smith, VJ (2006) Adapting k-means for supervised clustering. Applied Intelligence, 24 (3). pp. 219-226. ISSN 0924-669X

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Abstract

k-means is traditionally viewed as an algorithm for the unsupervised clustering of a heterogeneous population into a number of more homogeneous groups of objects. However, it is not necessarily guaranteed to group the same types (classes) of objects together. In such cases, some supervision is needed to partition objects which have the same label into one cluster. This paper demonstrates how the popular k-means clustering algorithm can be profitably modified to be used as a classifier algorithm. The output field itself cannot be used in the clustering but it is used in developing a suitable metric defined on other fields. The proposed algorithm combines Simulated Annealing with the modified k-means algorithm. We apply the proposed algorithm to real data sets, and compare the output of the resultant classifier to that of C4.5.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Vishal Gautam
Date Deposited: 20 May 2011 12:10
Last Modified: 22 Apr 2020 14:20
URI: https://ueaeprints.uea.ac.uk/id/eprint/23010
DOI: 10.1007/s10489-006-8513-8

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