A two-stage genetic algorithm for automatic clustering

He, Hong; Tan, Yonghong

doi:10.1016/j.neucom.2011.11.001

Cited by 105 publications

(39 citation statements)

References 20 publications

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“…According to [16,47], considering the total number of GPS data points is n, the population size is NIND (N), the number of iterations is MAXGEN (g), the number of attributes is m, the number of iterations for K-means clustering is x, and the maximum number of genes in a chromosome is K.…”

Section: Complexity Analysis Of Noiseclustmentioning

confidence: 99%

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Zhou

Shen

et al. 2017

IJGI

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Rapidly growing Global Positioning System (GPS) data plays an important role in trajectory and their applications (e.g., GPS-enabled smart devices). In order to employ K-means to mine the better origins and destinations (OD) behind the GPS data and overcome its shortcomings including slowness of convergence, sensitivity to initial seeds selection, and getting stuck in a local optimum, this paper proposes and focuses on a novel niche genetic algorithm (NGA) with density and noise for K-means clustering (NoiseClust). In NoiseClust, an improved noise method and K-means++ are proposed to produce the initial population and capture higher quality seeds that can automatically determine the proper number of clusters, and also handle the different sizes and shapes of genes. A density-based method is presented to divide the number of niches, with its aim to maintain population diversity. Adaptive probabilities of crossover and mutation are also employed to prevent the convergence to a local optimum. Finally, the centers (the best chromosome) are obtained and then fed into the K-means as initial seeds to generate even higher quality clustering results by allowing the initial seeds to readjust as needed. Experimental results based on taxi GPS data sets demonstrate that NoiseClust has high performance and effectiveness, and easily mine the city's situations in four taxi GPS data sets.

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Section: Complexity Analysis Of Noiseclustmentioning

confidence: 99%

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Zhou

Shen

et al. 2017

IJGI

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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]. Maulik and Bandyopadhyay [21] proposed a GA approach to clustering.…”

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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“…Syswerda [9] showed that the uniform crossover operator is more efficient when compared with twopoint crossover. Erbatur and Hasanc¸ ebi [10,11] suggested combining two crossover operators in their study about the effects of crossover operators on the behavior of GA. Mustafa Kaya [12] has introduced sequential and random mixed crossover operators and has compared them with other crossover operators on RC beam and the space truss problems Hong He and Yonghong Tan [13] have used a parallel crossover for automatic clustering of data without having number of clusters as input parameter. Their parallel crossover uses one point crossover and exchanges genes in length equal to smaller individual length.…”

Section: Related Workmentioning

confidence: 99%

Ccgdc: A New Crossover Operator For Genetic Data Clustering

Mohebpour¹,

Delavar

2014

J. Math. Computer Sci.

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Genetic algorithm is an evolutionary algorithm and has been used to solve many problems such as data clustering. Most of genetic data clustering algorithms just have introduced new fitness function to improve the accuracy of algorithm in evaluation of generated chromosomes. Crossover operator is the backbone of the genetic algorithm and should create better offspring and increase the fitness of population with maintaining the genetic diversity. A good crossover should result in feasible offspring chromosomes when we crossover feasible parent chromosomes. In this paper we introduce a new crossover operator for genetic data clustering. Experimental results show that clustered crossover for genetic data clustering (CCGDC) creates better offspring and increases the fitness of population and also will not produce illegal chromosome.

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A two-stage genetic algorithm for automatic clustering

Cited by 105 publications

References 20 publications

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

A Selection Process for Genetic Algorithm Using Clustering Analysis

Ccgdc: A New Crossover Operator For Genetic Data Clustering

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