摘要
把一个求解高维空间数据聚类问题转换为一个超图分割寻优问题,提出了一种基于超图模式的高维空间数据聚类方法。该方法不需要减少高维空间数据项的维数,直接用超图模式描述原始数据之间的关系,并通过选择适当的支持度阈值,有效祛除噪声点,保证数据聚类的质量。
This paper formulates the data clustering problem in a high-dimensional space as a hypergraph partition optimal problem , and proposes a method for clustering of data in a high dimensional space based on a hypergraph model. It does not require dimensionality reduction, as it uses the hypergraph model to represent relations among the original data items, and by finding the appropriate support threshold , people can filter out noise data from the clusters very effectively and control the quality of the cluseters. ;;;
出处
《计算机工程》
CAS
CSCD
北大核心
2002年第7期54-55,164,共3页
Computer Engineering
基金
广东省自然科学基金项目资助()990582
