Open AccessEnhancing the performance of social spider optimization with neighbourhood attraction algorithm
Author(s) -
K. Tamilarasi,
M. Gogulkumar,
K. Velusamy
Publication year - 2021
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 - 10.1088/1742-6596/1767/1/012017
Subject(s) - cluster analysis , computer science , attraction , neighbourhood (mathematics) , convergence (economics) , algorithm , data mining , mathematical optimization , artificial intelligence , mathematics , mathematical analysis , philosophy , linguistics , economics , economic growth
Data clustering is a well-known problem in order to identify the inherent structures and extracting the useful information. Recently, social spider optimization (SSO) algorithm is applied to solve a clustering problem. But, it may fall into premature convergence due to find improper nearest spiders in order to achieve the global solution. In this research paper, the neighbourhood attraction (NA) method is used to enhance the performance of the SSO clustering algorithm. In the experimental results, the proposed NA+SSO clustering method is producing better performance when compared with other conventional clustering algorithm.