International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 10, No. 5, 8 May 2018
Cover page and Table of Contents: PDF (size: 551KB)
A Novel Evolutionary Automatic Data Clustering Algorithm using Teaching-Learning-Based Optimization
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Author(s)
Index Terms
Teaching-Learning-Based Optimization, Automatic Data Clustering, Cluster Validity Indices, Meta-heuristics, Machine Learning, Evolutionary Algorithms, Multi-objective problems
Abstract
Teaching-Learning-Based Optimization (TLBO) is a contemporary algorithm being used as a novel, trustworthy, precise and robust optimization technique for global optimization over continuous spaces both constrained and unconstrained tribulations. TLBO works on the beliefs of teaching and learning and clearly justifies this pedagogy by highlighting the effect of power of a teacher on the output of learners in a class. This paper, explores the applicability of k-means unsupervised learning into TLBO with two endeavors, i.e. to automatically find the optimal number of naturally classified partition in the data without any prior information, and the other is to inspect the naturally classified partitions with cluster validity indices (CVIs) and endorse the goodness of clusters. The proposed automatic clustering algorithm using TLBO (AutoTLBO) pursues a novel evolutionary approach by incorporating the simple k-means algorithm and CVIs into TLBO to configure and validate automatic natural partition in datasets. This algorithm retains the core ideology of clustering to minimize the inter cluster distances and maximize the intra cluster distances among the data. Experimental analysis substantiates the openness of the anticipated method after inspecting suavest panoramic rendering over artificial and benchmark datasets.
Cite This Paper
Ramachandra Rao. Kurada, Karteeka Pavan. Kanadam, "A Novel Evolutionary Automatic Data Clustering Algorithm using Teaching-Learning-Based Optimization", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.5, pp.61-70, 2018. DOI:10.5815/ijisa.2018.05.07
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