A Comparative Study on Prejudiced Measurements of Da-tasets in three variants of Automatic Evolutionary Clustering using Teaching-Learning-Based Optimization

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Ramachandra Rao Kurada, Ramu Yadavalli, Sunil Pattem, Karteeka Pavan Kanadam

Abstract

This paper aims to conduct automatic non-predictive learning by dividing datasets into several subsets on their preju-diced measurements. To notarize paramount recitals in this direction, the work presented in this paper is captivated by con-ducting automatic evolutionary clustering with Teaching-Learning-Based Optimization (TLBO) to organize the collection of pat-terns into clusters based on similarity and by upholding minimum intra-cluster and maximum inter-cluster distances. This work has set a goal to determine the number of clusters automatically, enriching absolute positions of clusters with optimal size, shape, low computational time and minimum error rate. The cogitative content of this work is to investigate possibilities for the improvement in classical clustering algorithms with TLBO and evolutionary automated clustering cognitions. This article opti-mizes multiple objective functions simultaneously and evaluates clustering quality regarding the goodness-of-fit of the resulting clusters against the existing methods. This treatise advances a new point of view results by testing the performance of TLBO and its advancements with automatic clustering techniques across real-time and micro-array datasets.

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