关键词: 向量映射, 熵加权, 相似性度量, 混合数据集, 聚类分析

Abstract:

Due to the differences between categorical attributes and numerical attributes, researchers usually need to deal with these two types of attributes differently when designing clustering methods for mixed datasets. This increases the difficulty of designing and implementing clustering methods. Besides, the information contained in different attributes varies a lot, however, current methods treat different attributes equally. This paper proposes a weighted simplex-based mapping method for mixed data clustering. It maps the categorical attributes into high dimensional numerical attributes based on simplex theory, applies entropy theory to weight different attributes to establish the similarity measurement. The measurement is integrated with K-Means framework to form a clustering method. The experiments on 6 UCI mixed datasets show that the proposed method outperforms traditional mapping method and K-Prototype method, with 2.70% and 18.33% improvement in terms of accuracy.

Key words: vector mapping, entropy-based weight, similarity measurement, mixed datasets, clustering analysis