Incorporating Fuzziness to CLARANS

被引：0

作者：

Ghosh, Sampreeti ^{[1
]}

Mitra, Sushmita ^{[1
]}

机构：

[1] Indian Stat Inst, Ctr Soft Comp, Kolkata 700108, India

来源：

PATTERN RECOGNITION AND MACHINE INTELLIGENCE, PROCEEDINGS | 2009年 / 5909卷

关键词：

Data mining; CLARANS; medoid; fuzzy sets; clustering;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we propose a way of handling fuzziness while mining large data. Clustering Large Applications based on RANdomized Search (CLARANS) is enhanced to incorporate the fuzzy component. A new scalable approximation to the maximum number of neighbours, explored at a node, is developed. The goodness of the generated clusters is evaluated in terms of validity indices. Experimental results on various data sets is run to converge to the optimal number of partitions.

引用

页码：122 / 127

页数：6

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