A neighborhood search based cat swarm optimization algorithm for clustering problems
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RESEARCH PAPER
A neighborhood search based cat swarm optimization algorithm for clustering problems Hakam Singh1 · Yugal Kumar1 Received: 17 May 2019 / Revised: 17 January 2020 / Accepted: 20 February 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Clustering is an unsupervised technique that groups the similar data objects into a single subset using a distance function. It is also used to find the optimal set of clusters in a given dataset and each cluster consists of homogenous data objects. In present work, an algorithm based on cat swarm optimization (CSO) is adopted for finding the optimal set of cluster centers for allocating the data objects. Further, some improvements are also incorporated in CSO algorithm for improving clustering performance. These modifications are described as an improved solution search equation to improve convergence rate and an accelerated velocity equation for balancing exploration and exploitation processes of CSO algorithm. Moreover, a neighborhood-based search strategy is introduced to handle local optima problem. The performance of proposed algorithm is tested on eight real-life datasets and compared with well-known clustering algorithms. The simulation results showed that proposed algorithm provides quality results in comparison to existing clustering algorithms. Keywords Cat swarm optimization · Clustering · Machine learning · Meta-heuristics Abbreviations ABC Artificial bee colony ACO Ant colony optimization BATC Bat algorithm based clustering CABC Cooperative artificial bee colony CDC Count to dimensions CPSO Cooperative particle swarm optimization CS Cuckoo search CSO Cat swarm optimization DE Differential evolution FPAC Flower pollination algorithm based clustering GA Genetic algorithm GQCS Genetic quantum cuckoo search GWA Grey wolf algorithm HABC Hybrid artificial bee colony HCSDE Hybrid cuckoo search and differential evolution HS Harmony search KCPSO K-means chaotic particle swarm optimization * Yugal Kumar [email protected] Hakam Singh [email protected] 1
Department of Computer Science and Engineering, JUIT, Waknaghat, Himachal Pradesh, India
KFCM Kernel based fuzzy C-means KHM K-harmonic means KICS K-means and improved cuckoo search MO Magnetic optimization M-TLBO Modified-teaching learning based optimization PSO Particle swarm optimization QPSO Quantum-behaved particle swarm optimization R Rejected SA Simulated annealing SMP Seeking memory pool SRD Seeking range of selected dimension TLBO Teaching learning based optimization TS Tabu search
1 Introduction In the field of machine learning, clustering is a popular unsupervised data analysis technique. In clustering, data objects are grouped into different subsets in optimal manner [1–3]. Further, clustering is classified as partitional clustering, hierarchical clustering, grid-based clustering, density-based clustering and model-based clustering [4, 5]. The partitional clustering divides the dataset into several disjoint groups that are optim
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