Authors: Chafika Ramdane; Mohamed-Khireddine Kholladi
Addresses: MISC Laboratory, Computer Science Department, University of Constantine, PB 325, Ain El Bey Road, Constantine 25017, Algeria; Computer Science Department, University of Skikda, El-Hadaiek Road PB 26, Skikda, 21000, Algeria ' MISC Laboratory, Computer Science Department, University of Constantine, PB 325, Ain El Bey Road, Constantine 25017, Algeria
Abstract: In previous work, a novel approach to data clustering based on quantum evolutionary algorithm has been proposed. In a comparison to other evolutionary clustering algorithms, the approach showed a high performance in terms of effectiveness and quality of found clusters. Although the approach is sound, it tends to be trapped in local minima, which slows the convergence. The approach is based on degrees of belonging having a fixed relationship with the distance between the data points and the clusters. The fixed relationship ignores completely the dataset distribution. In this paper, we modify the approach to improve its convergence. We also modify the function calculating the degrees of belonging by taking inspiration from possibilistic clustering. Comparison has been done with approaches based on degrees of belonging like fuzzy, possibilistic, hybrid fuzzy possibilistic clustering and other quantum evolutionary algorithm. Results on both real and synthetic datasets show that the modifications brought to the approach enable a more efficient exploration of the search space which improves the convergence speed and quality.
Keywords: data clustering; quantum evolutionary clustering; convergence; degree of belonging; fuzzy clustering; possibilistic clustering; data mining.
International Journal of Data Analysis Techniques and Strategies, 2013 Vol.5 No.2, pp.175 - 197
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 04 May 2013 *