CNclustering: Clustering with Compatible nucleoids
Dissimilarity measure plays a very important role in traditional data clustering. In this paper, we extend the dissimilarity measure as compatible measure and present a new algorithm (CNclustering) based on this measure. The algorithm is a rigorous partition method, it first gets some compatible clusters with a Compclustering method as the initial nucleoids, then absorbs other objects by the absorbing step to form the final clusters. We use S20 and S200 data sets to demonstrate the clustering performance of the algorithm and get some consistent results.
clustering algorithm dissimilarity nucleoid compatible relation absorbing
Renxia Wan Lixin Wang Mingjun Wang Xiaoke Su Xiaoya Yan
College of Information Science and Technology, Donghua University, Shanghai 201620, P.R.China Zhanjiang Normal Univeristy, Zhanjiang, Guangdong, 524048, P.R.China
国际会议
第四届国际计算机新科技与教育学术会议(2009 4th International Conference on Computer Science & Education)
南京
英文
797-800
2009-07-25（万方平台首次上网日期，不代表论文的发表时间）