Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Abstract

The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide popularity and acceptance due to their success in solving and finding good quality solutions to a variety of complex real-world optimization problems. Clustering is considered one of the most important data analysis techniques in the domain of data mining. A clustering problem refers to the partitioning of unlabeled data objects into a certain number of clusters based on their attribute values or features, with the objective of maximizing intra-clusters homogeneity and inter-cluster heterogeneity. This paper presents an up-to-date survey of major nature-inspired metaheuristic algorithms that have been employed to solve automatic clustering problems. Further, a comparative study of several modified well-known global metaheuristic algorithms is carried out to solve automatic clustering problems. Also, three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, are proposed to deal with the task of automatic data clustering. In contrast to many of the existing traditional and evolutionary computational clustering techniques, the clustering algorithms presented in this paper do not require any predetermined information or prior-knowledge of the dataset that is to be classified, but rather they are capable of spontaneously identifying the optimal number of partitions of the data points during the course of program execution. Forty-one benchmarked datasets that comprise eleven artificial and thirty real world datasets are collated and utilized to evaluate the performances of the representative nature-inspired clustering algorithms. According to the extensive experimental results, comparisons and statistical significance, the firefly algorithm appeared to be more appropriate for better clustering of both low and high dimensional data objects than were other state-of-the-art algorithms. Further, an experimental study demonstrates the superiority of the three proposed hybrid algorithms over the standard stateof-the-art methods in finding meaningful clustering solutions to the problem at hand.

A Comparative Performance Study of Hybrid Firefly Algorithms for Automatic Data Clustering

“…All the different literature and comparative analyses results do point to the fact that the FA is a very efficient and robust metaheuristic algorithm for solving real-world problems. More so, the findings from Ezugwu [41] and Agbaje et al [51] on the promising performance of the FA for automatic clustering compelled us to go into this research to investigate further the superior performances of both the improved nutation based firefly algorithm and its hybrid variants for automatic data clustering.…”

“…Ezugwu [41] presented an extensive survey study of major nature-inspired metaheuristic algorithms that have been applied to solve automatic data clustering problems. Furthermore, the author carried out a comparative study of several modified well-known global metaheuristic algorithms to solve automatic clustering problems, of which three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, were employed to deal with the task of automatic clustering.…”

“…No record of a similar research focus in the literature exist as of the time of writing this paper. [41]) on the current ( ) Apply ABC updating formula (see [52]) on the current n ( ) Apply IWO updating formula (see [41]) on the current ( ) Apply TLBO updating formula (see [53]) on the current ( ) Update the global best solution in the whole population Evaluate the fitness value of each individual candidate solution Update the new value as the global best…”

“…The first stage engages the modified FA algorithm by randomly generating initial swarm, where the number of fireflies equal to the number of clusters and the swarm population is uniformly distributed across the dimension of the dataset, which in this case is the clustering problem search space. After the swarm initialization, the next task is the evaluation of the best swarm according to the fitness function determined by the DB and CS validity indices [41]. Note that the best swarm position, for example, represents the data point that achieves the minimum distance to the swarm from its previous searches.…”

“…The parameter configurations of ABC, IWO, PSO, and TLBO are further detailed in Table 2a. The details of the remaining datasets namely, Jain dataset, Pathbased dataset, Spiral dataset, and Thyroid can be obtained in [39] for Jain, [40] for both Pathbased and Spiral, and [41] for the Thyroid dataset. The twelve datasets configurations are summarized in Table 2b.…”