A Tabu search approach to the clustering problem

Al‐Sultan, Khaled S.

doi:10.1016/0031-3203(95)00022-r

Cited by 255 publications

(96 citation statements)

References 7 publications

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“…A new algorithm for solving this problem based on a TS technique. The algorithm obtained results that are better than the well-known k-means and the SA algorithms for many test problems [15].…”

Section: Introductionmentioning

confidence: 85%

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Danish¹,

Hassan²,

Badshah³

2018

IJCA

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Data clustering is a widespread data compression, vector quantization, data analysis and data mining technique. The principle objective of data clustering is to make clusters (or groups) such that data having high degree of similarity is gathered in the same cluster while data having high degree of dissimilarity is gathered in the different clusters and plays a key role for users to organize, summarize, and steer the data adequately. In this work Artificial Bee Colony (ABC) algorithm is applied to different size datasets. Results clearly show that ABC when applied on small size datasets were more effective than those of large size datasets in terms of intra-cluster distance, computation cycles and time required to complete those cycles.

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Section: Introductionmentioning

confidence: 85%

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Danish¹,

Hassan²,

Badshah³

2018

IJCA

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show abstract

“…These algorithms worked in a way that can explore all possible solutions in the problem search space or at least can escape falling in a local optimal solution. These 978-1-4244-4547-9/09/$26.00 c 2009 IEEE TENCON 2009 algorithms include simulated annealing as in [14], [15], tabu search [16], [17], genetic algorithm [18], bees optimization [19], particle swarm [20], and ant colony algorithm [7]. Recently, Ingram et al [10] provides a survey on the application of the HS algorithm in various FCM clustering problems.…”

Section: Related Workmentioning

confidence: 99%

Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images

Alia

Rajeswari

Ramachandram

et al. 2009

TENCON 2009 - 2009 IEEE Region 10 Conference

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Abstract-We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem. Our approach uses a metaheuristic search method called Harmony Search (HS) algorithm to produce near-optimal initial cluster centers for the FCM algorithm. We then demonstrate the effectiveness of our approach in a MRI segmentation problem. In order to dramatically reduce the computation time to find nearoptimal cluster centers, we use an alternate representation of the search space. Our experiments indicate encouraging results in producing stable clustering for the given problem as compared to using an FCM with randomly initialized cluster centers.

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“…Al-Sultan (1995) and Rao (1971) also used the within cluster squared Euclidean distances as the objective function that should be minimized.…”

Section: The Mathematical Model Of Clustering the Projectsmentioning

confidence: 99%

“…An initial solution Ac is found and the Jc is the corresponding objective function value (Our objective is the squared Euclidean intra cluster distances. Al-Sultan (1995) and Rao (1971) used the objective functions as the within cluster squared Euclidean distances).…”

Section: The Main Steps Of the Tabu Search Algorithmmentioning

confidence: 99%

Industrial Engineering

Harris¹

2000

Intelligent Systems, Control and Automation: Science and Engineering

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A Tabu search approach to the clustering problem

Cited by 255 publications

References 7 publications

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images

Industrial Engineering

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