Ant clustering algorithm with K-harmonic means clustering

Jiang, Hua; Yi, Shenghe; Li, Jing; Yang, Fengqin; Hu, Xin

doi:10.1016/j.eswa.2010.06.061

Cited by 72 publications

(18 citation statements)

References 14 publications

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“…Especially, clustering is done by different evolutionary approaches such as evolutionary programming, genetic algorithms, particle swarm optimization, ant colony algorithms [7] and bee colony algorithms [8]. Dong et al proposed a fuzzy clustering based on evolutionary programming.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis

Razavi¹,

Ebadati

Asadi

et al. 2015

Adaptation, Learning, and Optimization

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Abstract. Clustering as a formal, systematic subject in dissertations can be considered the most influential unsupervised learning problem; so, as every other problem of this kind, it deals with finding the structure in a collection of unlabeled data. One of the matters associated with this subject is undoubtedly determination of the number of clusters. In this chapter, an efficient grouping genetic algorithm is proposed under the circumstances of an anonymous number of clusters. Concurrent clustering with different number of clusters is implemented on the same data in each chromosome of grouping genetic algorithm in order to discern the accurate number of clusters. In subsequent iterations of the algorithm, new solutions with different clusters number or distinct accuracy of clustering are produced by application of efficient crossover and mutation operators that led to significant improvement of clustering. Furthermore, a local search by a special probability is applied in each chromosome of each new population in order to increase the accuracy of clustering.These special operators will lead to the successful application of the proposed method in the big data analysis. To prove the accuracy and the efficiency of the algorithm, its tested on various artificial and real data sets in a comparable manner. Most of the datasets consisted of overlapping clusters, but the algorithm could detect the proper number of all data sets with high accuracy of clustering. The consequences make the best evidence of the algorithms successful performance of finding an appropriate number of clusters and accomplishment of the best clusterings quality in comparison with others.

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Section: Introductionmentioning

confidence: 99%

An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis

Razavi¹,

Ebadati

Asadi

et al. 2015

Adaptation, Learning, and Optimization

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“…a new clustering algorithm using the Ant clustering algorithm with K-harmonic means clustering (ACAKHM) was proposed [12].…”

Section: Introductionmentioning

confidence: 99%

Data Clustering Based on an Efficient Hybrid of K-Harmonic Means, PSO and GA

Danesh

Naghibzadeh

Totonchi

et al. 2011

Transactions on Computational Collective Intelligence IV

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Abstract.Clustering is one of the most commonly techniques in Data Mining. Kmeans is one of the most popular clustering techniques due to its simplicity and efficiency. However, it is sensitive to initialization and easily trapped in local optima. K-harmonic means clustering solves the problem of initialization using a built-in boosting function, but it is suffering from running into local optima. Particle Swarm Optimization is a stochastic global optimization technique that is the proper solution to solve this problem. In this paper, PSOKHM not only helps KHM clustering escape from local optima but also overcomes the shortcoming of slow convergence speed of PSO. In this paper, a hybrid data clustering algorithm based on PSO and Genetic algorithm, GSOKHM, is proposed. We investigate local optima method in addition to the global optima in PSO, called LSOKHM. The experimental results on five real datasets indicate that LSOKHM is superior to the GSOKHM algorithm.

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“…Some relevant studies that have explored the problem of clustering using various approaches include evolutionary algorithms such as evolutionary programing [9], particle swarm optimization [10][11][12], ant colony algorithms [13,14], artificial bee colony [15], simulated annealing [16,17] and tabu search [18]. Conversely, there have been many attempts to use GAs to solve clustering applications [7,[19][20][21][22][23][24][25][26][27].…”

Section: Literature Reviewmentioning

confidence: 99%

A Selection Process for Genetic Algorithm Using Clustering Analysis

et al. 2017

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This article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA) is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis that clustering the population helps to preserve a selection pressure throughout the evolution of the population, a membership probability index is assigned to each individual following the clustering phase. Fitness scaling converts the membership scores in a range suitable for the selection function which selects the parents of the next generation. Two versions of the KGA process are presented: using a fixed number of clusters K (KGA f ) and via an optimal partitioning K opt (KGA o ) determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems.

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Ant clustering algorithm with K-harmonic means clustering

Cited by 72 publications

References 14 publications

An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis

An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis

Data Clustering Based on an Efficient Hybrid of K-Harmonic Means, PSO and GA

A Selection Process for Genetic Algorithm Using Clustering Analysis

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