Integrated Algorithm for Unsupervised Data Clustering Problems in Data Mining

Abstract

Unsupervised data clustering investigation is a standout amongst the most valuable apparatuses and an enlightening undertaking in data mining that looks to characterize homogeneous gatherings of articles depending on likeness and is utilized in numerous applications. One of the key issues in data mining is clustering data that have pulled in much consideration. One of the famous clustering algorithms is K-means clustering that has been effectively connected to numerous issues. Scientists recommended enhancing the nature of K-means, optimization algorithms were hybridized. In this paper, a heuristic calculation, Lion Optimization Algorithm (LOA), and Genetic Algorithm (GA) were adjusted for K-Means data clustering by altering the fundamental parameters of LOA calculation, which is propelled from the characteristic enlivened calculations. The uncommon way of life of lions and their participation attributes has been the essential inspiration for the advancement of this improvement calculation. The GA is utilized when it is required to reallocate the clusters using the genetic operators, crossover, and mutation. The outcomes of the examination of this calculation mirror the capacity of this methodology in clustering examination on the number of benchmark datasets from UCI Machine Learning Repository.

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“…It is one of the most frequently used techniques in classifying objects into numerous distinctive sets or dividing a dataset into clusters. Dividing data is a method for statistical data analysis, which is utilized in various fields such as data mining, bioinformatics, pattern recognition, machine learning, and image analysis [17]. Separating the data set into k subsets is a computational task called unsupervised learning.…”