聚类分析
稳健性(进化)
离群值
计算机科学
模糊聚类
数据挖掘
树冠聚类算法
CURE数据聚类算法
模糊逻辑
模式识别(心理学)
相关聚类
算法
人工智能
生物化学
基因
化学
作者
Jinglin Xu,Junwei Han,Kai Xiong,Feiping Nie
出处
期刊:International Joint Conference on Artificial Intelligence
日期:2016-07-09
卷期号:: 2224-2230
被引量:58
摘要
The partition-based clustering algorithms, like K-Means and fuzzy K-Means, are most widely and successfully used in data mining in the past decades. In this paper, we present a robust and sparse fuzzy K-Means clustering algorithm, an extension to the standard fuzzy K-Means algorithm by incorporating a robust function, rather than the square data fitting term, to handle outliers. More importantly, combined with the concept of sparseness, the new algorithm further introduces a penalty term to make the object-clusters membership of each sample have suitable sparseness. Experimental results on benchmark datasets demonstrate that the proposed algorithm not only can ensure the robustness of such soft clustering algorithm in real world applications, but also can avoid the performance degradation by considering the membership sparsity.
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