计算机工程与应用 ›› 2016, Vol. 52 ›› Issue (21): 88-93.

• 大数据与云计算 • 上一篇    下一篇

基于簇间距离自适应的软子空间聚类算法

邱云飞,狄龙娟   

  1. 辽宁工程技术大学 软件学院,辽宁 葫芦岛 125105
  • 出版日期:2016-11-01 发布日期:2016-11-17

Soft subspace clustering algorithm based on self-adaption of intercluster distance

QIU Yunfei, DI Longjuan   

  1. Software?College,?Liaoning?Technical?University,?Huludao,?Liaoning?125105, China
  • Online:2016-11-01 Published:2016-11-17

摘要: For the uncertain problem that intercluster distance(intercluster separation) influences on clustering in the soft subspace clustering process, a self-adaptive soft subspace clustering algorithm has been proposed based on the compactness of intracluster compactness and the intercluster distance. Minimize the intracluster compactness, and meanwhile maximize the intercluster distance based on the framework of classical k-means clustering algorithm. And a new way of computing clusters’ centers and features weighting is gotten by derivation. This way overcomes the sensitive defect of input parameters, realizes the self-adaptive learning, and obtains better clustering results.

关键词: adaptivity, intercluster distance, soft subspace clustering, high-dimensional data

Abstract: For the uncertain problem that intercluster distance(intercluster separation) influences on clustering in the soft subspace clustering process, a self-adaptive soft subspace clustering algorithm has been proposed based on the compactness of intracluster compactness and the intercluster distance. Minimize the intracluster compactness, and meanwhile maximize the intercluster distance based on the framework of classical k-means clustering algorithm. And a new way of computing clusters’ centers and features weighting is gotten by derivation. This way overcomes the sensitive defect of input parameters, realizes the self-adaptive learning, and obtains better clustering results.

Key words: adaptivity, intercluster distance, soft subspace clustering, high-dimensional data