An Algorithm for Clustering Data Based on Rough Set Theory

Shangzhi Wu
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引用次数: 1

Abstract

A variety of cluster analysis techniques exist to group objects having similar characteristics. While there have been recent advances in algorithms for clustering data, some are unable to handle uncertainty in the clustering process while others have stability issues. This paper proposes a new algorithm for clustering data based on rough set theory, which has the ability to handle the uncertainty in the clustering process.
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基于粗糙集理论的数据聚类算法
存在各种各样的聚类分析技术来对具有相似特征的对象进行分组。虽然最近在聚类数据的算法方面取得了进展,但有些算法无法处理聚类过程中的不确定性,而另一些算法则存在稳定性问题。本文提出了一种新的基于粗糙集理论的数据聚类算法,该算法具有处理聚类过程中的不确定性的能力。
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