Bio-inspired multiobjective clustering optimization: A survey and a proposal

Abstract

Multiobjective clustering techniques have been used to simultaneously consider several complementary aspects of clustering quality. They optimize two or more cluster validity indices simultaneously, they lead to high-quality results, and have emerged as attractive and robust alternatives for solving clustering problems. This paper provides a brief review of bio-Inspired multiobjective clustering, and proposes a bee-inspired multiobjective optimization (MOO) algorithm, named cOptBees-MO, to solve multiobjective data clustering problems. In its survey part, a brief tutorial on MOO and multiobjective clustering optimization (MOCO) is presented, followed by a review of the main works in the area. Particular attention is given to the many objective functions used in MOCO. To evaluate the performance of the algorithm it was executed for various datasets and the results presented high quality clusters, diverse solutions an the automatic determination of a suitable number of clusters.

Multi-objective PSO with Pareto Neighborhood topology for Clustering

“…A multi-objective artificial bee optimization algorithm called cOptBees-MO was proposed in [8], in which two scenarios were used. The first scenario tries to optimize two cluster validity indices (Silhouette and Overall Cluster Deviation), while the second attempts to optimize three cluster validity indices: the I-Index, the Con-Index and the Sym-Index.…”

“…A Multi-Objective Quantum Moth Flame Optimization MOQMFO algorithm which was proposed by [33]. MOQMFO combines the features of the Quantum theory and the multi- The MOPSO-PN used clusters validities indices [8] as objectives functions in the process of multi-objective clustering in order to help to find an optimal number of clusters and an optimal clustering solution and F-measure [31] as metric for evaluation of the final obtained clustering solutions. Based on this metric, the performance of MOPSO-PN is then compared to the predefined state of art algorithms.…”

“…The cluster deviation (Dec(C)) as presented in [8] [14] and is the metric evaluating the compactness of a cluster. Dec(C) is the overall summed Euclidean distances between cluster centers and their corresponding data objects and is calculated as follows:…”

“…I-index measures the geometric repartition of clusters related to their centers using the Euclidean distance, [8] [31]:…”

“…In [9], a non-dominated sorting policy is used to detect the pareto fronts candidates then a crowding distance sorting is used to reduce the repository size for a better detection of the best solution. The selection of the best solution from the "Pareto-Optimal-Solutions" is made through a technique based on the distance between the set of points of the Pareto and an ideal point called Utopia-Point; the best solution is then defined as the closest point to the Utopia-Point [8].As showed in Figure 1, green circles represent the Pareto-Optimal-Solutions (Repository members), yellow circle presents the utopia point, which is the intersection between the line parallel to F2 and which passes through the solution-point with the minimum value of F1 and the line parallel to F1 and which passes through the solution-point with the minimum value of F2, the best solution which is defined as the closest point to the Utopia-Point is presented by the orange circle.…”