Efficiently Finding the Optimum Number of Clusters in a Dataset with a New Hybrid Cellular Evolutionary Algorithm

Abstract

A challenge in hybrid evolutionary algorithms is to employ efficient strategies to cover all the search space, applying local search only in actually promising search areas; on the other hand, clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. Some existing methods use evolutionary algorithms with cluster validation index as the objective function. In this article, a new cellular evolutionary algorithm based on a hybrid model of global and local heuristic search is proposed for the same task, and extensive experimentation is done with different datasets and indexes.

Food recommendation system based on nutritional needs of human beings and user preferences

“…There are many methods used to solve this problem in a smart way by determining the best number of clusters for each type of data, and one of these methods is the silhouette algorithm [23,24].…”