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Hybrid Air Mass Collision Based Optimization Algorithm for Data Cluster Problems

In data mining, clustering is an important data analysis concept. It plays a vital role in extracting the useful hidden knowledge from large input datasets. This unsupervised technique partitions the input dataset into groups called clusters. The data objects mapping is done into clusters such clusters should maintain similarity between the objects within same cluster and dissimilarity between the data objects in different clusters. In this process factors like distance measuring techniques, initial conditions and criterion functions playa key role in finding optimal clusters of data. Many optimization algorithms have come into existence to resolve these types of optimization problems. But still finding optimal clusters is a big challenging task. This work presents hybrid version of the recently devised nature-inspired algorithm i.e. Tornadogenesis Optimization Algorithm (TOA) for solving data clustering problems using BB-BC. We framed this work in two phases wherein the first phase testing for optimization performance on 23 standard mathematical benchmark functions took place, in the second phase numerical ability is tested by applying hybridized Tornadogenesis Optimization Algorithm (HTOA) on 10 real-world data clustering problems. In addition to that various distance measuring techniques used to test the improvement in clustering performance. We portrayed the obtained results in tabular and graphical forms. Various analysis and comparisons have been made and found that the performance of proposed HTOA is good at solving data clustering problems using Euclidean distance measuring technique.