Open AccessFuzzy Kernel Based Effective Clustering Techniques in Analyzing Heterogeneous Databases
Author(s) -
S. Kannan,
M. Siva,
R. Devi,
S. Ramathilagam
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1344/1/012039
Subject(s) - cluster analysis , data mining , kernel (algebra) , fuzzy clustering , fuzzy logic , benchmark (surveying) , computer science , field (mathematics) , mathematics , flame clustering , database , artificial intelligence , cure data clustering algorithm , geography , geodesy , pure mathematics , combinatorics
The aim of this paper is to introduce an effective fuzzy clustering technique based kernel function to find appropriate subgroups in heterogeneous databases. This paper introduces the effective fuzzy clustering that incorporates weighted bias field information, kernel distance, possibilistic memberships and fuzzy memberships into memberships equation and prototype equation. The effectiveness and efficiency of the proposed clustering techniques have been shown through the experimental results on benchmark heterogeneous databases.