关键词: 数据挖掘, 聚类, BIRCH算法, 混合属性

Abstract: Data clustering is an important issue in data mining.Many real-world data have both continuous attributes and categorical attributes，which are usually called heterogeneous attributes.However，most of the existing mining algorithms can manipulate only continuous attributes or categorical attributes.Simply omitting categorical or continuous attributes may lose important information about the data and decrease the mining quality.Some other algorithms which can manipulate continuous attributes and categorical attributes have low efficiency，because of a lot of attributes.This paper proposes a novel approach for clustering data with heterogeneous features based on BIRCH.Experimental results on public data sets show that the proposed algorithm is robust.

Key words: data mining, clustering, BIRCH algorithm, heterogeneous attribute