Approximation Algorithms for K-Modes Clustering
| dc.creator | He, Zengyou | |
| dc.date | 2006-03-30 | |
| dc.date.accessioned | 2026-07-07T07:05:57Z | |
| dc.date.available | 2026-07-07T07:05:57Z | |
| dc.description | In this paper, we study clustering with respect to the k-modes objective function, a natural formulation of clustering for categorical data. One of the main contributions of this paper is to establish the connection between k-modes and k-median, i.e., the optimum of k-median is at most twice the optimum of k-modes for the same categorical data clustering problem. Based on this observation, we derive a deterministic algorithm that achieves an approximation factor of 2. Furthermore, we prove that the distance measure in k-modes defines a metric. Hence, we are able to extend existing approximation algorithms for metric k-median to k-modes. Empirical results verify the superiority of our method. | |
| dc.description | 7 pages | |
| dc.identifier | https://arxiv.org/abs/cs/0603120 | |
| dc.identifier | http://arxiv.org/abs/cs/0603120 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/109849 | |
| dc.subject | Artificial Intelligence | |
| dc.title | Approximation Algorithms for K-Modes Clustering | |
| dc.type | text |