---
title: 'K-ANMI: A Mutual Information Based Clustering Algorithm for Categorical Data'
url: https://www.emergentmind.com/papers/0511013
type: paper
arxiv_id: '0511013'
arxiv_url: https://arxiv.org/abs/0511013
published: '2005-11-03'
categories:
- cs.AI
- cs.DB
---

# K-ANMI: A Mutual Information Based Clustering Algorithm for Categorical Data

## Abstract

Clustering categorical data is an integral part of data mining and has attracted much attention recently. In this paper, we present k-ANMI, a new efficient algorithm for clustering categorical data. The k-ANMI algorithm works in a way that is similar to the popular k-means algorithm, and the goodness of clustering in each step is evaluated using a mutual information based criterion (namely, Average Normalized Mutual Information-ANMI) borrowed from cluster ensemble. Experimental results on real datasets show that k-ANMI algorithm is competitive with those state-of-art categorical data clustering algorithms with respect to clustering accuracy.