A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering

Yin, Minghao; Hu, Yanmei; Yang, Fengqin; Li, Xiangtao; Gu, Wei

doi:10.1016/j.eswa.2011.01.018

Cited by 108 publications

(41 citation statements)

References 14 publications

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“…KMeans and K-Harmonic with Fish School Search Algorithm [19] provides more optimized results than KFSS. IGSA-KHM algorithm [20] not only helps the KHM clustering escape from local optima but also overcomes the slow convergence speed of the IGSA. CGA [21] is clustering based Genetic Algorithm with polygamy selection and dynamic population control technique.…”

Section: Bio-inspired Optimization Techniquesmentioning

confidence: 99%

Comparative Analysis of Hybrid K-Mean Algorithms on Data Clustering

Kaur¹,

Aggarwal²

2017

IJCATR

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Data clustering is a process of organizing data into certain groups such that the objects in the one cluster are highly similar but dissimilar to the data objects in other clusters. K-means algorithm is one of the popular algorithms used for clustering but k-means algorithm have limitations like it is sensitive to noise ,outliers and also it does not provides global optimum results. To overcome its limitations various hybrid kmeans optimization algorithms are presented till now. In hybrid k-means algorithms the optimization techniques are combined with k-means algorithm to get global optimum results. The paper analyses various hybrid k-means algorithms i.e. Firefly, Bat with k-means algorithm, ABCGA etc. The Comparative analysis is performed using different data sets obtained from UCI machine learning repository. The performance of these hybrid k-mean algorithms is compared on the basis of output parameters like CPU time, purity etc. The result of Comparison shows that which k-means hybrid algorithm is better in obtaining cluster with less CPU time and also with high accuracy.

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Section: Bio-inspired Optimization Techniquesmentioning

confidence: 99%

Comparative Analysis of Hybrid K-Mean Algorithms on Data Clustering

Kaur¹,

Aggarwal²

2017

IJCATR

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“…A GSA has also been applied to K-harmonic mean clustering algorithm for complicated clustering problems. It has shown efficient result but it requires long running time [46]. Further, a GSA has been designed to extract highly correlated bi-cluster of web usage data [47].…”

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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“…Applications of GSA algorithm are in power system [35][36][37][38][39][40][41][42], economic dispatch problem [43,44], Wessinger's equation [45], fuzzy system [46,47], forecasting of future oil demand [48], slope stability analysis [49], clustering [50][51][52], prototype classification [53], feature selection [54], web services [55], PID controller [56], antenna application [47], and so forth.…”

Section: Apo Apomentioning

confidence: 99%

Physics-Inspired Optimization Algorithms: A Survey

Biswas

Mishra

Tiwari

et al. 2013

Journal of Optimization

109

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Natural phenomenon can be used to solve complex optimization problems with its excellent facts, functions, and phenomenon. In this paper, a survey on physics-based algorithm is done to show how these inspirations led to the solution of well-known optimization problem. The survey is focused on inspirations that are originated from physics, their formulation into solutions, and their evolution with time. Comparative studies of these noble algorithms along with their variety of applications have been done throughout this paper.

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A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering

Cited by 108 publications

References 14 publications

Comparative Analysis of Hybrid K-Mean Algorithms on Data Clustering

Comparative Analysis of Hybrid K-Mean Algorithms on Data Clustering

Gravitational Search Algorithms in Data Mining: A Survey

Physics-Inspired Optimization Algorithms: A Survey

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