2CHCOKC - A 2D Chaotic based Hybrid Coyote Optimization with K-Means for Clustering the Massive Datasets to Enhance the Datasearch Ability

Data clustering is broadly exploited across a broad spectrum of industries, including teaching, healthcare, and innumerable decentralized business settings. The miscellaneous datasets are effectively handled and analyzed through various classification procedures to enhance the eminence of essential data sharing in decentralized environments. K-Means represents a widely recognized and intuitive clustering method employed to segment a set of data values into a predetermined count of clusters. It has proven successful in addressing numerous practical clustering challenges. However, the critical aspects of choosing the initial cluster centers and congregating to local optima present significant limitations in the K-Means method. Various optimization methods are implemented to enhance the clustering's accuracy. Here, a 2D Chaotic based Hybrid Coyote Optimization with K-Means for Clustering (2CHCOKC) method is implemented and utilized to categorize a substantial amount of varied datasets, with the goal of enhancing the data retrieval proficiency and K-Means performance. The central aim of the 2CHCOKC approach is to accomplish resourceful and exact scattering of data values within a dataset. This is accomplished by utilizing 2D chaotic map for establishing populations. The addition of 2D chaotic map is anticipated to enrich the calculation and employment skills of search agents within the optimization framework for attaining the optimal and exact classification of centers and members within a cluster. The development of the 2CHCOKC methodology using MATLAB 2022a tool on five huge datasets highlights the exceptional effectiveness of the method. This is marked when comparing it to former approaches such as K-Means, ALO, GO, and CO. This comparison is established through various aspects containing F-Measure, Accuracy, Standard Deviation, Root Mean Square Error, Time Complexity, Purity Index, Intra-Cluster Distance, and Statistical Explication.