A combined approach for clustering based on K-means and gravitational search algorithms

Hatamlou, Abdolreza; Abdullah, Salwani; Nezamabadi‐pour, Hossein

doi:10.1016/j.swevo.2012.02.003

Cited by 188 publications

(67 citation statements)

References 38 publications

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“…Variants of GSA also developed based on the concept of antigravity. In GSA randomly created candidate solutions for data clustering problem then the interaction have been made to each and other via Newton's gravitation law to search problem space [26], [27]. The WOA (Whale Optimization Algorithm) [28] has been devised based on the foraging strategy of hump back whales.…”

Section: Related Workmentioning

confidence: 99%

“…Like the other state-of-the-art and popular population based meta-heuristic optimization techniques such as GSA, DE [12], PSO [15], WOA [25] and advanced variants IWOA [26] and NPMOA [27] of WOA, makes use of a population to investigate the problem space. Population based mechanism bring into play the probability of obtaining optimal solution and abscond from local optima increases.…”

Section: A Experiments 1: Mathematical Benchmark Functionsmentioning

confidence: 99%

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Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies

Saidala¹,

Devarakonda²

2018

IJISA

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Abstract-Computational Intelligence (CI) is an as of emerging area in addressing complex real world problems. The WOA has taken its root from the collective intelligent foraging behavior of humpback whales (Megaptera Novaeangliae). The standard WOA is suffers from the selection of best agent while whales searching and encircling prey. This research paper deals with the multi-swarm cooperative strategies for finding the best agents which balances the two phase's exploration and exploitation. The performance of invoked Multi-Swarm cooperative strategies into standard WOA i.e, MsWOA is first benchmarked on a set of 23 standard mathematical benchmark function problems which includes 7 UniModal, 6 Multi-modal and 10 fixed dimension multimodal functions. The obtained graphical and statistical results have been portrayed along with the previously established techniques. The obtained results depicts that the proposed cooperative strategies for WOA outperforms in solving optimization problems of standard benchmark functions. This paper also studies the numerical efficiency of proposed techniques on the problem of data clustering where 10 real data clustering problems have been taken from data repository https://archive.ics.uci.edu.data. Statistical results for the obtained cluster centroids, intra-cluster distances and inter-cluster distances confirms that the cooperative strategies for best whale agent selection improves the performance WOA for function optimization problems as well as data clustering problems.

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Section: Related Workmentioning

confidence: 99%

Section: A Experiments 1: Mathematical Benchmark Functionsmentioning

confidence: 99%

Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies

Saidala¹,

Devarakonda²

2018

IJISA

View full text Add to dashboard Cite

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“…A GSA has been used with K-means clustering algorithm. In this algorithm, First Kmeans is used as initial population generator for GSA to improve the convergence speed of GSA then the improved GSA is used with K-means to improve cluster quality [44]. A GSA has been hybridized with heuristic search where GSA was used to produce initial solutions and the heuristic search was used to improve the initial solutions for clustering problem [45].…”

Section: Gsa In Clusteringmentioning

confidence: 99%

Gravitational Search Algorithms in Data Mining: A Survey

Patil¹,

Ratnoo²

2017

International Journal of Advanced Research in Computer and Comm

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A Gravitational Search Algorithm is a meta-heuristic search algorithm inspired by Newton"s laws of motion and gravitation force. Gravitational search based algorithms have attracted the attention of data mining community due to their ability to effectively search large and complex search spaces as is the case with the domain of data mining and knowledge discovery. Gravitational search algorithms have been applied to the data mining tasks like classification, clustering objects and mining association rules etc. This paper presents a survey on the applications of Gravitational Search based algorithms in data mining and indicates the scope of further research on using GSAs in the field of data mining.

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“…Th is algorith m provides the good searching ability to find global optimu m but in the last iterations the performance of algorith m suffers due to slow searching speed. To improve the searching ability of GSA, Abdolreza Hatamlou and Salwani Abdullah et al [79] have proposed a hybrid algorithm GSA-KM based on the GSA & K-means for clustering problem because k-means is simp le and fast algorithm. The hybrid algorith m provides better performance in co mparison of original GSA by two ways.…”

Section: Gsa Hybridization and Applicationsmentioning

confidence: 99%

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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A combined approach for clustering based on K-means and gravitational search algorithms

Cited by 188 publications

References 38 publications

Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies

Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies

Gravitational Search Algorithms in Data Mining: A Survey

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

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