重新设计的模糊C奇异点聚类算法

2016 2nd International Conference on Contemporary Computing and Informatics (IC3I) Pub Date : 2016-12-01 DOI:10.1109/IC3I.2016.7918790

Terence Johnson, S. Singh, Anuradha Sharma

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引用次数: 1

摘要

与传统的模糊C奇异点聚类算法仅利用欧氏距离寻找奇异点和隶属函数将奇异点分组成簇相比，重新设计的模糊C奇异点聚类算法利用隶属函数来寻找奇异点并建立元素与不同聚类的相似度。重新设计的算法与传统的模糊c奇异点聚类方法具有相似的聚类质量和收敛速度。

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The redesigned Fuzzy C Strange points clustering algorithm

The redesigned Fuzzy C Strange points clustering algorithm uses the membership function to find the strange points and also to establish the degree of likeness of elements to different clusters as opposed to the traditional fuzzy c strange points clustering algorithm which uses the Euclidean distance to find the strange points and membership function only to group the points into clusters. The redesigned algorithm was observed to give similar quality of clusters and also converge with the same speed of execution as the orthodox fuzzy c strange points clustering method.

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2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)

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