聚类分析
计算机科学
数据挖掘
子空间拓扑
模式识别(心理学)
高维数据聚类
标杆管理
相关聚类
人工智能
CURE数据聚类算法
业务
营销
作者
Zhaohong Deng,Kup‐Sze Choi,Fu-Lai Chung,Shitong Wang
标识
DOI:10.1016/j.patcog.2009.09.010
摘要
While within-cluster information is commonly utilized in most soft subspace clustering approaches in order to develop the algorithms, other important information such as between-cluster information is seldom considered for soft subspace clustering. In this study, a novel clustering technique called enhanced soft subspace clustering (ESSC) is proposed by employing both within-cluster and between-class information. First, a new optimization objective function is developed by integrating the within-class compactness and the between-cluster separation in the subspace. Based on this objective function, the corresponding update rules for clustering are then derived, followed by the development of the novel ESSC algorithm. The properties of this algorithm are investigated and the performance is evaluated experimentally using real and synthetic datasets, including synthetic high dimensional datasets, UCI benchmarking datasets, high dimensional cancer gene expression datasets and texture image datasets. The experimental studies demonstrate that the accuracy of the proposed ESSC algorithm outperforms most existing state-of-the-art soft subspace clustering algorithms.
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