---
title: "Central Clustering of Categorical Data with Automated Feature Weighting"
authors:
  - "Lifei Chen"
  - "Shengrui Wang"
doi: ""
url: "https://ijcai.org/proceedings/2013/190"
pdf: "https://ijcai.org/proceedings/2013/0190.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "1260-1265"
track: ""
keywords:
---

# Central Clustering of Categorical Data with Automated Feature Weighting

Lifei Chen, Shengrui Wang

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 1260-1265.

## Abstract

The ability to cluster high-dimensional categorical data is essential for many machine learning applications such as bioinfomatics. Currently, central clustering of categorical data is a difficult problem due to the lack of a geometrically interpretable definition of a cluster center. In this paper, we propose a novel kernel-density-based definition using a Bayes-type probability estimator. Then, a new algorithm called k-centers is proposed for central clustering of categorical data, incorporating a new feature weighting scheme by which each attribute is automatically assigned with a weight measuring its individual contribution for the clusters. Experimental results on real-world data show outstanding performance of the proposed algorithm, especially in recognizing the biological patterns in DNA sequences.