Clustering Mixed Numeric and Categorical Data: A Cluster Ensemble Approach

TL;DR

This paper introduces a divide-and-conquer ensemble clustering method for datasets with mixed numeric and categorical attributes, effectively combining existing algorithms to improve clustering performance on real-world data.

Contribution

It presents a flexible framework that integrates various clustering algorithms for mixed data types, addressing limitations of traditional methods.

Findings

Outperforms existing clustering algorithms on real datasets

Effectively handles datasets with mixed attribute types

Demonstrates the superiority of the ensemble approach

Abstract

Clustering is a widely used technique in data mining applications for discovering patterns in underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either numeric or categorical attributes. However, datasets with mixed types of attributes are common in real life data mining applications. In this paper, we propose a novel divide-and-conquer technique to solve this problem. First, the original mixed dataset is divided into two sub-datasets: the pure categorical dataset and the pure numeric dataset. Next, existing well established clustering algorithms designed for different types of datasets are employed to produce corresponding clusters. Last, the clustering results on the categorical and numeric dataset are combined as a categorical dataset, on which the categorical data clustering algorithm is used to get the final clusters. Our contribution in this paper is to provide an algorithm framework for the mixed attributes clustering problem, in which existing clustering algorithms can be easily integrated, the capabilities of different kinds of clustering algorithms and characteristics of different types of datasets could be fully exploited. Comparisons with other clustering algorithms on real life datasets illustrate the superiority of our approach.