A tabu search approach for the minimum sum-of-squares clustering problem

Liu, Yongguo; Zhang, Yi; Wu, Hong; Ye, Mao; Chen, Kefei

doi:10.1016/j.ins.2008.01.022

Cited by 53 publications

(26 citation statements)

References 42 publications

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“…The Table 1 entities into disjoint clusters such that entities of each cluster are similar. Clustering techniques have been emp loyed in many areas such as pattern recognition [3,4], informat ion retrieval [70,71], data mining [72,73], and many mo re. Large nu mber of nature inspired algorith ms is applied on the data clustering problem such as ABC [11], PSO [74], KMH [75], A CO [76], K-means [77], BB-BC [34] etc.…”

Section: Gsa Hybridization and Applicationsmentioning

confidence: 99%

“…Nature inspired algorithms are the latest state of art algorith ms & works well with optimization problems as well as other problems than the classical methods because classical methods are inflexible in nature. It has been proved by many researchers that nature inspired algorith ms are convenient to solve complex co mputational problems such as to optimize objective functions [1,2], pattern recognition [3,4], control functions [5,6], image processing [7,8], filter modeling [9,10], clustering [3], classification [11] etc. In last one and half decade s everal nature inspired algorithms have been developed such as Particle swarm optimization (PSO), Genetic A lgorith m (GA), Simu lated Annealing (SA), Ant colony optimization (ACO), Art ificial Bee colony (A BC) optimization, Big Bang Big Crunch (BB-BC) etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification

Kumar¹,

Sahoo²

2014

IJISA

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Abstract− Natural phenomenon's and swarms behavior are the warm area of research among the researchers. A large number of algorithms have been developed on the account of natural phenomenon's and swarms behavior. These algorithms have been implemented on the various computational problems for the sake of solutions and provided significant results than conventional methods but there is no such algorithm which can be applied for all of the computational problems. In 2009, a new algorithm was developed on the behalf of theory of gravity and was named gravitational search algorithm (GSA) for continuous optimization problems. In short span of time, GSA algorithm gain popularity among researchers and has been applied to large number of problems such as clustering, classification, parameter identification etc. This paper presents the compendious survey on the GSA algorithm and its applications as well as enlightens the applicability of GSA in data clustering & classification.

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Section: Gsa Hybridization and Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification

Kumar¹,

Sahoo²

2014

IJISA

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“…But how to fine-tune the membership degrees of objects with respect to different clusters so as to improve the object distribution among different clusters did not receive enough attention in their works. After reviewing the related work, we find that many research works focus on employing tabu search to solve the hard clustering problem (Al-sultan, 1995;Liu et al, 2008;Sung and Jin, 2000), but relatively few attempts have been made to solve the fuzzy clustering problem with tabu search. So, it is necessary to further improve the performance of the tabu search fuzzy clustering method.…”

Section: Related Workmentioning

confidence: 99%

“…climbing hard clustering method is popular (Liu et al, 2008;Selim and Ismail, 1984). It is known that hard clustering algorithms assign each object to one and only one cluster which are inappropriate for the data sets where the boundaries between clusters may not be well defined (Amiri et al, 2009;Chang et al, 2009;Jarhoui et al, 2007;Laszlo and Mukherjee, 2006;Zhang et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

On using tabu search for fuzzy clustering analysis

Liu¹,

Wu²,

Shen³

2011

Sci. Res. Essays

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Clustering is an important technique for discovering the inherent structure in a given data set without any 'priori' knowledge. Fuzzy clustering analysis is to assign objects to a given number of clusters with respect to some criteria such that each object may belong to more than one cluster with different degrees of membership. In this article, a new fuzzy clustering method based on tabu search called Improved Tabu Search Fuzzy Clustering (ITSFC) is proposed to find the proper clustering of data sets. In the ITSFC approach, a fuzzy c-means operation is developed to fine-tune the clustering solution obtained in the process of iterations and a divide-and-merge operation is designed to establish the neighborhood. Experimental results on two artificial and four real life data sets are given to illustrate the superiority of the proposed algorithm over a tabu search clustering algorithm and an artificial bee colony clustering algorithm.

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“…However, to obtain better clustering results, researchers have recently focused on the use of metaheuristic algorithms like genetic algorithms (Kivijarvi et al, 2003;Handl & Knowles, 2007;Chang et al, 2009;Xiao et al, 2010), tabu search (Al-Sultan, 1995;Liu et al, 2008), simulated annealing (Sun et al, 1994), ant colony optimization (ACO) algorithms (Shelokar et al, 2004;Runkler, 2005) and hybrid algorithms (Pirzadeh et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Clustering fuzzy objects using ant colony optimization

Ahmadizar¹,

Farahani²

2013

10.5267/j.ijiec

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This paper deals with the problem of grouping a set of objects into clusters. The objective is to minimize the sum of squared distances between objects and centroids. This problem is important because of its applications in different areas. In prior literature on this problem, attributes of objects have often been assumed to be crisp numbers. However, since in many realistic situations object attributes may be vague and should better be represented by fuzzy numbers, we are interested in the generalization of the minimum sum-of-squares clustering problem with the attributes being fuzzy numbers. Specifically, we consider the case where an object attribute is a triangular fuzzy number. The problem is first formulated as a fuzzy nonlinear binary integer programming problem based on a newly proposed dissimilarity measure, and then solved by developing and demonstrating a problem-specific ant colony optimization algorithm. The proposed algorithm is evaluated by computational experiments.

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A tabu search approach for the minimum sum-of-squares clustering problem

Cited by 53 publications

References 42 publications

A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification

A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification

On using tabu search for fuzzy clustering analysis

Clustering fuzzy objects using ant colony optimization

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