Clustering analysis through artificial algae algorithm

Abstract

Clustering analysis is widely used in many areas such as document grouping, image recognition, web search, business intelligence, bio information, and medicine. Many algorithms with different clustering approaches have been proposed in the literature. As they are easy and straightforward, partitioning methods such as K-means and K-medoids are the most commonly used algorithms. These are greedy methods that gradually improve clustering quality, highly dependent on initial parameters, and stuck a local optima. For this reason, in recent years, heuristic optimization methods have also been used in clustering. These heuristic methods can provide successful results because they have some mechanism to escape local optimums. In this study, for the first time, Artificial Algae Algorithm was used for clustering and compared with ten well-known bio-inspired metaheuristic clustering approaches. The proposed AAA clustering efficiency is evaluated using statistical analysis, convergence rate analysis, Wilcoxon's test, and different cluster evaluating measures ranking on 25 well-known public datasets with different difficulty levels (features and instances). The results demonstrate that the AAA clustering method provides more accurate solutions with a high convergence rate than other existing heuristic clustering techniques.