articles
http://link.springer.com/10.1023%2FA%3A1009769707641
1998-09
1998-09-01
283-304
en
research_article
false
2019-04-10T18:25
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
https://scigraph.springernature.com/explorer/license/
The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present two algorithms which extend the k-means algorithm to categorical domains and domains with mixed numeric and categorical values. The k-modes algorithm uses a simple matching dissimilarity measure to deal with categorical objects, replaces the means of clusters with modes, and uses a frequency-based method to update modes in the clustering process to minimise the clustering cost function. With these extensions the k-modes algorithm enables the clustering of categorical data in a fashion similar to k-means. The k-prototypes algorithm, through the definition of a combined dissimilarity measure, further integrates the k-means and k-modes algorithms to allow for clustering objects described by mixed numeric and categorical attributes. We use the well known soybean disease and credit approval data sets to demonstrate the clustering performance of the two algorithms. Our experiments on two real world data sets with half a million objects each show that the two algorithms are efficient when clustering large data sets, which is critical to data mining applications.
Artificial Intelligence and Image Processing
3
Information and Computing Sciences
doi
10.1023/a:1009769707641
ACSys CRC, CSIRO Mathematical and Information Sciences, GPO Box 664, 2601, Canberra, ACT, Australia
2
1573-756X
1384-5810
Data Mining and Knowledge Discovery
Huang
Zhexue
readcube_id
80ca94e1f3e994798ee64caf2a3bffd5f3bbfdf815fd7dd72a6727507d4e6122
Springer Nature - SN SciGraph project
dimensions_id
pub.1027035492