Abstract
Clustering is an important tool for data mining and knowledge discovery that helps in revealing hidden structures and “clusters” found in large data sets. Fuzzy C-means (FCM) is considered to be popular data clustering method due to its capability of clustering the datasets that are uncertain, vague and/or are otherwise difficult to cluster. Although, noted both for its simplicity of implementation and its output validity, performance of FCM usually gets affected in case of poor initialization resulting in the algorithm getting trapped into a local optimum. To overcome this shortcoming, the present study proposes a novel clustering algorithm called fuzzy magnetic optimization clustering (Fuzzy-MOC) which embeds the concept of fuzzy clustering into magnetic optimization algorithm. In Fuzzy-MOC, the data points apply force directly to the magnetic particles due to which the particles change their positions in the feature space. Magnetic particles are attracted by their neighbours assumed to be in a lattice like structure. The proposed algorithm is evaluated on a set of 16 benchmark datasets taken from the UCI Machine Learning Repository including high dimensional gene expression dataset. Experimental results demonstrate that Fuzzy-MOC outperforms the other state-of-the-art algorithms in terms of different performance metrics like F1, accuracy, purity and RI measure.
Similar content being viewed by others
References
Arzeno NM, Vikalo H (2015) Semi-supervised affinity propagation with soft instance-level constraints. IEEE Trans Pattern Anal Mach Intell 37:1041–1052. https://doi.org/10.1109/TPAMI.2014.2359454
Babu GP, Murty MN (1994) Clustering with evolution strategies. Pattern Recognit 27:321–329. https://doi.org/10.1016/0031-3203(94)90063-9
Bandyopadhyay S, Maulik U (2002) An evolutionary technique based on K-means algorithm for optimal clustering in RN. Inf Sci (N Y) 146:221–237. https://doi.org/10.1016/S0020-0255(02)00208-6
Barabási A-L, Albert R, Jeong H (2000) Scale-free characteristics of random networks: the topology of the world-wide web. Phys A Stat Mech its Appl 281:69–77. https://doi.org/10.1016/S0378-4371(00)00018-2
Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10:191–203. https://doi.org/10.1016/0098-3004(84)90020-7
Everitt BS, Landau S, Leese M, Stahl D (2011) Cluster analysis. Wiley series in probability and statistics. Wiley, Chichester
Gionis A, Mannila H, Tsaparas P (2007) Clustering aggregation. ACM Trans Knowl Disc Data (TKDD) 1(1):4
Guha S, Rastogi R, Shim K (2000) Rock: a robust clustering algorithm for categorical attributes. Inf Syst 25:345–366. https://doi.org/10.1016/S0306-4379(00)00022-3
Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam
Holme P, Kim BJ (2002) Growing scale-free networks with tunable clustering. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Top 65:2–5. https://doi.org/10.1103/PhysRevE.65.026107
Hruschka ER, Campello RJGB, Freitas AA, de Carvalho ACPLF (2009) A survey of evolutionary algorithms for clustering. IEEE Trans Syst Man Cybern Part C Appl Rev 39:133–155. https://doi.org/10.1109/TSMCC.2008.2007252
Izakian H, Abraham A (2011) Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst Appl 38:1835–1838. https://doi.org/10.1016/j.eswa.2010.07.112
Jain AK, Law MHC (2005) Data clustering: a user’ s dilemma. Pattern Recognit Mach Intell 3776:1–10. https://doi.org/10.1007/11590316_1
Kushwaha N, Pant M (2018) Link based BPSO for feature selection in big data text clustering. Future Gen Comput Syst. https://doi.org/10.1016/j.future.2017.12.005
Kushwaha N, Pant M, Kant S, Kumar V (2017) Magnetic optimization algorithm for data clustering. Pattern Recognit Lett 0:1–7. https://doi.org/10.1016/j.patrec.2017.10.031
Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1–18. https://doi.org/10.1016/j.swevo.2013.11.003
Pang W, Wang KP, Zhou CG, Dong LJ (2004) Fuzzy discrete particle swarm optimization for solving traveling salesman problem. In: The fourth international conference on computer and information technology, CIT’04. IEEE, pp 796–800
Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge
Shelokar PS, Jayaraman VK, Kulkarni BD (2004) An ant colony approach for clustering. Anal Chim Acta 509:187–195. https://doi.org/10.1016/j.aca.2003.12.032
Shen H, Yang J, Wang S, Liu X (2006) Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets. Soft Comput 10:1061–1073. https://doi.org/10.1007/s00500-005-0043-5
Sun L, Guo C (2014) Incremental affinity propagation clustering based on message passing. IEEE Trans Knowl Data Eng 26:2731–2744
Tayarani MH, Akbarzadeh TMR (2008) Magnetic optimization algorithms a new synthesis. In: 2008 IEEE congress in evolutionary computing CEC 2008, pp 2659–2664. https://doi.org/10.1109/CEC.2008.4631155
Xu R, Wunsch D II (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16:645–678. https://doi.org/10.1109/TNN.2005.845141
Xu R, Member S, Ii DW (2005) Survey of clustering algorithms 16:645–678
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kushwaha, N., Pant, M. Fuzzy magnetic optimization clustering algorithm with its application to health care. J Ambient Intell Human Comput 15, 1053–1062 (2024). https://doi.org/10.1007/s12652-018-0941-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-018-0941-x