CLUSTER ANALYSIS USING HYBRID SOFT COMPUTING TECHNIQUES

被引：0
|
作者：
Purushotham, Swarnalatha [1 ]
Tripathy, B. K. [1 ]
机构：
[1] VIT Univ, Vellore, Tamil Nadu, India
来源：
IIOAB JOURNAL | 2016年 / 7卷 / 05期
关键词：
Cluster Centre Matrix; Fuzzy Membership; Lower and Upper Approximation; Objective Function; Satellite Images;
D O I：
暂无
中图分类号：
Q5 [生物化学]; Q7 [分子生物学];
学科分类号：
071010 ; 081704 ;
摘要：
In a given set of data values with several attributes, similar data points can be clubbed together using a clustering architecture that uses global prototypes. These subset prototypes are exchanged so that a communication link is established between different clustering units. In this paper, a detailed clustering methodology is developed by combining both rough and fuzzy set techniques. This methodology shall be used to formulate a grouping of randomly generated unsupervised data that considers the integration of collaborative clustering in fuzzy data sets.
引用
收藏
页码：265 / 274
页数：10
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