计算机科学 ›› 2013, Vol. 40 ›› Issue (1): 191-194.

• 人工智能 • 上一篇    下一篇

基于阴影集的粗糙模糊可能性C均值聚类算法

汪海良,佘 堃,周明天   

  1. (电子科技大学计算机科学与工程学院 成都610054)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Shadowed Sets-based Rough Fuzzy Possibilistic C-means Clustering

  • Online:2018-11-16 Published:2018-11-16

摘要: 相对于硬聚类算法,软聚类算法可以更好地表示具有不精确边界的类簇。粗糙集和模糊集均是用于描述不确定数据的有效的数学工具,二者互为补充。研究人员已经将粗糙集和模糊集的概念相结合,并应用到聚类算法中,提出了粗糙模糊可能性C均值聚类算法。而文中通过引入阴影集,有效地解决了粗糙模糊可能性C均值聚类算法中的C均值选择问题。

关键词: 粗糙集,阴影集,FCM, PCM

Abstract: It has been shown that soft clustering is advantageous over hard clustering in describing clusters without crisp boundaries. Both rough sets and fuzzy sets arc effective mathematical tools in handling uncertainty. As claimed in many studies,they are complementary. The theories of rough sets and fuzzy sets have been integrated into clustering algorithms,such as rough fuzzy possibilistic Gmeans clustering (RFPCM). In this study, we introduced the shadowed sets optimization theory and proposed an objective method to select the threshold in RFPCM.

Key words: Rough sets, Shadow sets, FCM, PCM

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