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

Abdolreza Hatamlou, Salwani Abdullah, Hossein Nezamabadi-Pour

Research output: Contribution to journalArticle

130 Citations (Scopus)

Abstract

Clustering is an attractive and important task in data mining that is used in many applications. Clustering refers to grouping together data objects so that objects within a cluster are similar to one another, while objects in different clusters are dissimilar. K-means is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. The gravitational search algorithm (GSA) is one effective method for searching problem space to find a near optimal solution. In this paper, we present a hybrid data clustering algorithm based on GSA and k-means (GSA-KM), which uses the advantages of both algorithms. The GSA-KM algorithm helps the k-means algorithm to escape from local optima and also increases the convergence speed of the GSA algorithm. We compared the performance of GSA-KM with other well-known algorithms, including k-means, genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), honey bee mating optimization (HBMO), particle swarm optimization (PSO) and gravitational search algorithm (GSA). Five real and standard datasets from the UCI repository have been used to demonstrate the results of the algorithms. The experimental results are encouraging in terms of the quality of the solutions and the convergence speed of the proposed algorithm.

Original languageEnglish
Pages (from-to)47-52
Number of pages6
JournalSwarm and Evolutionary Computation
Volume6
DOIs
Publication statusPublished - Oct 2012

Fingerprint

K-means
Search Algorithm
Clustering
Data Clustering
K-means Algorithm
Speed of Convergence
Centroid
Trap
Simulated Annealing
Grouping
Repository
Particle Swarm Optimization
Clustering Algorithm
Data Mining
Efficient Algorithms
Optimal Solution
Genetic Algorithm
Optimization
Experimental Results
Ant colony optimization

Keywords

  • Clustering
  • Gravitational search algorithm
  • K-means

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

Cite this

A combined approach for clustering based on K-means and gravitational search algorithms. / Hatamlou, Abdolreza; Abdullah, Salwani; Nezamabadi-Pour, Hossein.

In: Swarm and Evolutionary Computation, Vol. 6, 10.2012, p. 47-52.

Research output: Contribution to journalArticle

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