Data clustering based on hybrid K-harmonic means and modifier imperialist competitive algorithm

Abdeyazdan, Marjan

doi:10.1007/s11227-013-1053-1

Cited by 15 publications

(11 citation statements)

References 23 publications

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“…In their study, CS algorithm is used with Levy flight and is found to be performing better compared to the other two methods. ICAKHM is a novel method on the basis of hybrid K-harmonic means algorithm and an improved version of the imperialist competitive algorithm (ICA) [2]. This version of ICA method uses the genetic operators of crossover and mutation to prevent the premature convergence helping KHM to evade local optima problem similar to many other evolutionary algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Step 2) The re-estimation step: the cluster centroids are recalculated from the instances assigned to the cluster according to Equation (2).…”

Section: Repeat Steps 1 Andmentioning

confidence: 99%

“…The basic ideas applied are the aggressive reproduction strategy of cuckoo and usage of Levy flights. Cuckoo Search algorithm is being widely used in engineering optimization problems [2] with exceptionally good results. PSO is a versatile population based stochastic optimization technique.…”

Section: Introductionmentioning

confidence: 99%

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An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Bouyer

2016

Foundations of Computing and Decision Sciences

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Abstract. Among the data clustering algorithms, k-means (KM) algorithm is one of the most popular clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to initial centers and it has the local optima problem. K-harmonic-means (KHM) clustering algorithm solves the initialization problem of k-means algorithm, but it also has local optima problem. In this paper, we develop a new algorithm for solving this problem based on an improved version of particle swarm optimization (IPSO) algorithm and KHM clustering. In the proposed algorithm, IPSO is equipped with Cuckoo Search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence and escape from local optima. IPSO updates positions of particles based on a combination of global worst, global best with personal worst and personal best to dynamically be used in each iteration of the IPSO. The experimental result on five real-world datasets and two artificial datasets confirms that this improved version is superior to k-harmonic means and regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy and correctness while markedly improving the processing time.

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Section: Related Workmentioning

confidence: 99%

“…Step 2) The re-estimation step: the cluster centroids are recalculated from the instances assigned to the cluster according to Equation (2).…”

Section: Repeat Steps 1 Andmentioning

confidence: 99%

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An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Bouyer

2016

Foundations of Computing and Decision Sciences

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“…In their study, the CS algorithm is used with Levy flight and is found to be performing better compared to the other two methods. ICAKHM is a novel method on the basis of a hybrid KHM algorithm and a modified version of the imperialist competitive algorithm (ICA) [2] . This version of the ICA method uses the genetic operators of crossover and mutation to prevent the premature convergence, helping KHM to evade the local optima problem similar to many other evolutionary algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…In the simulation process, C 1 and C 2 are adjusted with different values between [1,2] ; and ω min , ω max are set with different values between [0, 1]. For instance, Table 2 shows the various assigned values for these parameters.…”

Section: Simulation Setupsmentioning

confidence: 99%

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Bouyer

Farajzadeh

2015

Journal of Intelligent Systems

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AbstractAmong the data clustering algorithms, the k-means (KM) algorithm is one of the most popular clustering techniques because of its simplicity and efficiency. However, KM is sensitive to initial centers and it has a local optima problem. The k-harmonic means (KHM) clustering algorithm solves the initialization problem of the KM algorithm, but it also has a local optima problem. In this paper, we develop a new algorithm for solving this problem based on a modified version of particle swarm optimization (MPSO) algorithm and KHM clustering. In the proposed algorithm, MPSO is equipped with the cuckoo search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence, and escape from local optima. MPSO updates the positions of particles based on a combination of global worst, global best with personal worst, and personal best to dynamically be used in each iteration of the MPSO. The experimental result on eight real-world data sets and two artificial data sets confirms that this modified version is superior to KHM and the regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy, and correctness while markedly improving the processing time.

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A Novel K-harmonic Means Clustering Based on Enhanced Firefly Algorithm

Zhou

Zhu

Zhang

2015

Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques

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Data clustering based on hybrid K-harmonic means and modifier imperialist competitive algorithm

Cited by 15 publications

References 23 publications

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

A Novel K-harmonic Means Clustering Based on Enhanced Firefly Algorithm

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