Fuzzy C-means algorithm with gravitational search algorithm in spatial data mining

Sheshasaayee, Ananthi; Sridevi, D.

doi:10.1109/inventive.2016.7823259

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…A clustering algorithm based on Gravitational Search Algorithm and Genetic Operators has also been suggested in which a GSA has been used to find optimal clusters and genetic operators is used to improve the performance of the GSA [49]. A GSA has been applied to find optimum clusters centers for fuzzy c-means clustering algorithm [50]. Han et al (2017) have applied an improved version of GSA called Bird Flock Gravitational Search Algorithm (BFGSA) for data clustering problem [51].…”

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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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 Modified Gravitational Search Algorithm for Function Optimization

Zhu

Wang

et al. 2019

IEEE Access

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Gravitational search algorithm (GSA) is a population-based heuristic algorithm, which is inspired by Newton's laws of gravity and motion. Although GSA provides a good performance in solving optimization problems, it has a disadvantage of premature convergence. In this paper, the concept of repulsive force is introduced and the definition of exponential Kbest is used in a new version of GSA, which is called repulsive GSA with exponential Kbest (EKRGSA). In this algorithm, heavy particles repulse or attract all particles according to distance, and all particles search the solution space under the combined action of repulsive force and gravitational force. In this way, the exploration ability of the algorithm is improved and a proper balance between exploration and exploitation is established. Moreover, the exponential Kbest significantly decreases the computational time. The proposed algorithm is tested on a set of benchmark functions and compared with other algorithms. The experimental results confirm the high efficiency of EKRGSA.

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Fault Diagnosis Method Based on Kernel Fuzzy C-Means Clustering with Gravitational Search Algorithm

2018

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)

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Fuzzy C-means algorithm with gravitational search algorithm in spatial data mining

Cited by 5 publications

References 13 publications

Gravitational Search Algorithms in Data Mining: A Survey

Gravitational Search Algorithms in Data Mining: A Survey

A Modified Gravitational Search Algorithm for Function Optimization

Fault Diagnosis Method Based on Kernel Fuzzy C-Means Clustering with Gravitational Search Algorithm

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