2005/09/05 by Zengyou He, Xiaofei Xu, He, Zengyou +3
Computer Science · #Advanced Clustering Algorithms Research #Artificial Intelligence (cs.AI) #Data Management and Algorithms #Data Mining Algorithms and Applications #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.cs/0509011
14 pages
arxiv created 2005/09/05 · openalex publication_date 2005/09/05 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.