Data clustering using eDE, an enhanced differential evolution algorithm with fuzzy c-means technique
Data clustering using eDE, an enhanced differential evolution algorithm with fuzzy c-means technique
Clustering is the way toward sorting out items into groups whose individuals are comparative somehow. It isa gathering of articles that are intelligent inside, yet unmistakably not at all like the items having a place with differentgroups. Clustering of data plays a major part in efficient customer segmentation, organization of documents, informationretrieval, extraction of topics, classification, collaborative filtering, visualization, and indexing. In the area of informationretrieval systems, evolutionary algorithms work in a robust and efficient manner for clustering. To overcome the problemof local maxima, various nature-inspired metaheuristic algorithms like particle swarm optimization, artificial bee colony,and firefly algorithms are considered. In this work, a variant of a differential evolution algorithm named enhanceddifferential evolution (eDE) is created. eDE is incorporated with the fuzzy c-means technique to perform clustering ofdata.
___
- [1] Coello CAC, Veldhuizen VDA, Lamont GB. Evolutionary Algorithms for Solving Multi-Objective Problems (Vol. 242). New York, NY, USA: Kluwer Academic, 2002.
- [2] Krishna K, Murty NM. Genetic K-means algorithm. IEEE T Syst Man Cy B 1999; 29: 433-439.
- [3] Bandyopadhyay S, Ujjwal M. An evolutionary technique based on K-means algorithm for optimal clustering in RN. Inform Sciences 2002; 146: 221-237.
- [4] Bosman PAN, Thierens D. The balance between proximity and diversity in multi-objective evolutionary algorithms. IEEE T Evolut Comput 2003; 7: 174-188.
- [5] Hruschka ER, de Castro LN, Campello RJGB. Evolutionary algorithms for clustering gene-expression data. In: IEEE Data Mining; 1–4 November 2004; Brighton, UK. New York, NY, USA: IEEE. pp. 403-406.
- [6] Tasoulis DK, Plagianakos VP, Vrahatis MN. Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima. In: IEEE 2005 Evolutionary Computation; 2–5 September 2005; Edinburgh, UK. New York, NY, USA: IEEE. pp. 1847-1854.
- [7] Singh G, Deb K. Comparison of multi-modal optimization algorithms based on evolutionary algorithms. In: ACM Genetic and Evolutionary Computation Conference; 8–12 July 2006; Seattle, WA, USA. New York, NY, USA: ACM. pp. 1305-1312.
- [8] Handl J, Knowles J. An evolutionary approach to multiobjective clustering. IEEE T Evolut Comput 2007; 11: 56-76.
- [9] Das S, Abraham A, Konar A. Automatic clustering using an improved differential evolution algorithm. IEEE T Syst Man Cy A 2008; 38: 218-237.
- [10] Hruschka ER, Campello RJGB, Freitas AA, de Carvalho ACPLF. A survey of evolutionary algorithms for clustering. IEEE T Syst Man Cy C 2009; 39: 133-155
- [11] Das S, Sil S. Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm. Inform Sciences 2010; 180: 1237-1256.
- [12] Karaboga D, Ozturk C. A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 2011; 11: 652-657.
- [13] Chen CY, Ye F. Particle swarm optimization algorithm and its application to clustering analysis. In: IEEE Electrical Power Distribution Networks; 2–3 May 2012; Tehran, Iran. New York, NY, USA: IEEE. pp. 789-794.
- [14] Hatamlou A. Black hole: a new heuristic optimization approach for data clustering. Inform Sciences 2013; 222: 175-184.
- [15] Xu KS, Kliger M, Hero AO 3rd. Adaptive evolutionary clustering. Data Min Knowl Disc 2014; 28: 304-336.
- [16] Ozturk C, Hancer E, Karaboga D. Dynamic clustering with improved binary artificial bee colony algorithm. Appl Soft Comput 2015; 28: 69-80.
- [17] Ramadas M, Abraham A, Kumar S. Using data clustering on ssFPA/DE-a search strategy flower pollination algorithm with differential evolution. In: Springer Hybrid Intelligent Systems; 21–23 November 2016; Marrakech, Morocco. Berlin, Germany: Springer. pp. 539-550.
- [18] Dunn JC. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybernetics 1973; 3: 32-57.
- [19] Bezdek JC. Cluster validity with fuzzy sets. J Cybernetics 1973; 3: 58-73.
- [20] Xie XL, Beni G. A validity measure for fuzzy clustering. IEEE T Pattern Anal 1991; 13: 841-847.