Abstract
This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets.
Highlights
The current study proposes an automatic clustering algorithm, namely, hybrid multi-objective particle swarm optimization with simulated annealing (MOPSOSA), which deals with different sizes, shapes, and dimensions of datasets and an unknown number of clusters
More information about the results of the cluster number and F-measure values of GenClustMOO, GenClustPESA2, multi-objective clustering with automatic K determination (MOCK), VGAPS, KM, and single-linkage clustering technique (SL) on the specified datasets can be acquired from Saha and Bandyopadhyay [8]
This research proposed a new automatic multi-objective clustering algorithm MOPSOSA based on a hybrid multi-objective particle swarm algorithm and multi-objective simulated annealing
Summary
Data Availability Statement: All relevant data are within the paper and its Supporting Information files. The current study proposes an automatic clustering algorithm, namely, hybrid multi-objective particle swarm optimization with simulated annealing (MOPSOSA), which deals with different sizes, shapes, and dimensions of datasets and an unknown number of clusters. The MOPSOSA algorithm utilizes KM method [14] to improve the selection of the initial particle position because of its significance in the overall performance of the search process It creates a large number of Pareto optimal solutions through a trade-off between the three different validity indices.
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