An Efficient Social Spider Optimization for Data Clustering using Data Vector Representation
T. Ravichandran1, B. Janet2, A. V. Reddy3
1T. Ravichandran, Department of Computer Applications, National Institute of Technology, Trichy, India.
2B. Janet, Department of Computer Applications, National Institute of Technology, Trichy, India.
3A. V. Reddy, Department of Computer Applications, National Institute of Technology, Trichy, India.
Manuscript received on March 16, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 2553-2557 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8523038620/2020©BEIESP | DOI: 10.35940/ijrte.F8523.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this article, we propose a new clustering algorithm namely an efficient social spider optimization for data clustering using data vector representation (ESSODCDI). It uses a data vector representation for each spider so that its memory requirements can be reduced. Unlike other nature-inspired algorithms, it requires lesser memory requirements. We find that its clustering results are by far better than those of other nature-inspired algorithms.
Keywords: Nature-inspired Algorithms, Social Spider Optimization, Clustering, Memory Requirements, Data Vector.
Scope of the Article: Approximation And Randomized Algorithms.