Metaheuristics Algorithm for Search Result Clustering

This chapter presents Metaheuristics algorithms for search result clustering and gives the various results on the basis of different algorithms. These algorithms are recognized as promising swarm intelligence methods which are used to solve machine learning problems such as text clustering applications. This survey reviews various relevant papers on the Metaheuristics algorithms for search result clustering. This also reports advantages as well as disadvantages, comparison between various methods, and also potential future work of various papers. There are various algorithms in Metaheuristics in which some improvements are made to extract the result. Various algorithms are combined together to perform clustering as well. The main aim of this chapter is to improve the efficiency of the PSO algorithm which can improve the clustering of datasets, and this can be done by using a new fitness function as well as taking variable inertia weight.