A possibilistic Fuzzy c-means algorithm based on improved Cuckoo search for data clustering
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https://doi.org/10.54939/1859-1043.j.mst.CSCE6.2022.3-15Keywords:
Possibilistic fuzzy c-means; Cuckoo Search; Improved Cuckoo Search; Fuzzy clustering.Abstract
Possibilistic Fuzzy c-means (PFCM) algorithm is a powerful clustering algorithm. It is a combination of two algorithms Fuzzy c-means (FCM) and Possibilistic c-means (PCM). PFCM algorithm deals with the weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in the case of coincidence clusters. However, PFCM still has a common weakness of clustering algorithms that is easy to fall into local optimization. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be stable and high-efficiency. In this study, we propose a hybrid method encompassing PFCM and improved Cuckoo search to form the proposed PFCM-ICS. The proposed method has been evaluated on 4 data sets issued from the UCI Machine Learning Repository and compared with recent clustering algorithms such as FCM, PFCM, PFCM based on particle swarm optimization (PSO), PFCM based on CS. Experimental results show that the proposed method gives better clustering quality and higher accuracy than other algorithms.
References
[1]. Jain, K., Murthy, M.N., Flynn, P.J.: “Data Clustering: A Review”. ACM Computing Surveys 31(3), 264–323 (1999) DOI: https://doi.org/10.1145/331499.331504
[2]. Xu, R., Wunsch, D.C.: “Clustering”, 2nd edn., pp. 1–13. IEEE Press, John Wiley and Sons, Inc. (2009)
[3]. I. H. Witten and E. Frank, “Data Mining-Pratical Machine Learning Tools and Techniques”, 3rd ed. Morgan Kaufmann Publishers, Inc., (2011). DOI: https://doi.org/10.1016/B978-0-12-374856-0.00001-8
[4]. S. Mitra and T. Acharya, “Data Mining: Multimedia”, Soft Computing, and Bioinformatics. Wiley, (2003).
[5]. Anil K. Jain. “Data clustering: 50 years beyond K-means”. Pattern Recognition Letters; 31(8):651-666, (2010). DOI: https://doi.org/10.1016/j.patrec.2009.09.011
[6]. J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms”. Plenum Press, New York, (1981). DOI: https://doi.org/10.1007/978-1-4757-0450-1
[7]. R. Krishnapuram and J. Keller, “A possibilistic approach to clustering,” IEEE Trans. Fuzzy Systems, vol. 1. no. 2, pp. 98–110, (1993). DOI: https://doi.org/10.1109/91.227387
[8]. N. R. Pal, K. Pal, and J. C. Bezdek, “A possibilistic fuzzy c-means clustering algorithm,” IEEE Trans. Fuzzy Systems, vol. 13, no. 4, pp. 517–530, (2005). DOI: https://doi.org/10.1109/TFUZZ.2004.840099
[9]. Colanzi, T.E., Assunção, W.K.K.G., Pozo, A.T.R., Vendramin, A.C.B.K., Pereira, D.A.B., Zorzo, C.A., de Paula Filho, P.L.: “Application of Bio inspired Metaheuristics in the Data Clustering Problem”. Clei Electronic Journal 14(3) (2011) DOI: https://doi.org/10.19153/cleiej.14.3.5
[10]. Yang, X.-S., Deb, S.: “Cuckoo Search via Levy Flights”. In: Proc. of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), India, pp. 210–214. IEEE Publications, USA (2009) DOI: https://doi.org/10.1109/NABIC.2009.5393690
[11]. Yang, X.-S., Deb, S.: “Engineering Optimisation by Cuckoo Search”. International Journal of Mathematical Modelling and Numerical Optimisation 1(4-30), 330–343 (2010) DOI: https://doi.org/10.1504/IJMMNO.2010.035430
[12]. M. Jamil, H.J. Zepernick, X.S. Yang. “Levy Flight Based Cuckoo Search Algorithm for Synthesizing Cross-Ambiguity Functions”. IEEE Military Communications Conference (Milcom), San Diego, CA; 823–828, (2013). DOI: https://doi.org/10.1109/MILCOM.2013.145
[13]. X.-S. Yang, “Nature-inspired Optimization Algorithm”, first ed. Elsevier, MA, USA, (2014).
[14]. Jothi, R., Vigneshwaran, A.: “An Optimal Job Scheduling in Grid Using Cuckoo Algorithm”. International Journal of Computer Science and Telecommunications 3(2), 65–69 (2012).
[15]. Noghrehabadi, A., Ghalambaz, M., Ghalambaz, M., Vosough, A.: “A hybrid Power Series –Cuckoo Search Optimization Algorithmto Electrostatic Deflection of Micro Fixed-fixed Actuators”. International Journal of Multidisciplinary Sciences and Engineering 2(4), 22–26 (2011)
[16]. L.D. Coelho, C.E. Klein, S.L. Sabat, V.C. Mariani. “Optimal chiller loading for energy conservation using a new differential cuckoo search approach”. Energy. 2014; 75 (1) :237–243. DOI: https://doi.org/10.1016/j.energy.2014.07.060
[17]. A. Natarajan, S. Subramanian, K. Premalatha. “A comparative study of cuckoo search and bat algorithm for Bloom filter optimisation in spam filtering”. Int. J. Bio-Inspir. Comp; 4 (2): 89–99, (2012). DOI: https://doi.org/10.1504/IJBIC.2012.047179
[18]. Liyu, Mliang. “New Meta-heuristic Cuckoo Search Algorithm”. Systems engineering. (08),64-69, (2012).
[19]. Yang X-S, Deb S. “Cuckoo search:recent advances and applications”, Neural Computing and Applications, 24(1):169-174, (2014). DOI: https://doi.org/10.1007/s00521-013-1367-1
[20]. Huynh Thi Thanh Binh, Nguyen Thi Hanh, La Van Quan, Nilanjan Dey, “Improved Cuckoo Search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks”, Neural Comput & Applic, (2016).
[21]. U. Maulik, S. Bandyopadhyay, “Performance evaluation of some clustering algorithms and validity indices”, IEEE Trans. Pattern Anal. Mach. Intell. 24 (12), 1650–1654, (2002). DOI: https://doi.org/10.1109/TPAMI.2002.1114856
[22]. J.C. Bezdek, N. Pal, “Some new indexes of cluster validity”, IEEE Trans. Syst. Man Cybern. 28 (3), 301–315, (1998). DOI: https://doi.org/10.1109/3477.678624
[23]. C.H. Chou, M.C. Su, E. Lai, “A new cluster validity measure and its application to image compression”, Pattern Anal. Appl. 7 (2), 205–220, (2004). DOI: https://doi.org/10.1007/s10044-004-0218-1
[24]. Z. Wang, A.C. Bovik, “Mean squared error: love it or leave it? A new look at signal fidelity measures”, IEEE Signal Process. Mag. 98–117, (2009). DOI: https://doi.org/10.1109/MSP.2008.930649
[25]. J. Cao, Z. Wu, J. Wu, and H. Xiong, “SAIL: Summationbased incremental learning for informationtheoretic text clustering,” IEEE Transactions on Cybernetics, vol. 43, no. 2, pp. 570–584, (2013). DOI: https://doi.org/10.1109/TSMCB.2012.2212430
[26]. Dinh Sinh Mai, Long Thanh Ngo, Le Hung Trinh, Hani Hagras, "A hybrid interval type-2 semi-supervised possibilistic fuzzy c-means clustering and particle swarm optimization for satellite image analysis", Information Sciences, (2020).
[27]. Z. Huang, M.K. Ng, “A fuzzy k-modes algorithm for clustering categorical data”, IEEE Trans. Fuzzy Syst. 7 (4), 446–452, (1999). DOI: https://doi.org/10.1109/91.784206
[28]. M. Mareli, B. Twala, "An adaptive Cuckoo search algorithm for optimisation", Applied Computing and Informatics, 2210-8327 / (2017).
[29]. Y. He, K. Zhang, Z. Sun, “A possibilistic fuzzy c-means clustering algorithm based on improved particle swarm optimization”, Journal of Computational Information Systems 10(18):7845-7857, (2014).
[30]. J. Kennedy, R. Eberhart, “Particle swarm optimization”, in: IEEE International Conference on Neural Networks, pp. 1942–1948, (1995).
[31]. Artur Starczewski, Adam Krzyżak, “Performance Evaluation of the Silhouette Index”, ICAISC 2015: Artificial Intelligence and Soft Computing, pp 49-58, (2015). DOI: https://doi.org/10.1007/978-3-319-19369-4_5
[32]. Dae-WonKim, Kwang H.Lee, DoheonLee, “On cluster validity index for estimation of the optimal number of fuzzy clusters”, Pattern Recognition, Volume 37, Issue 10, Pages 2009-2025, (2004). DOI: https://doi.org/10.1016/j.patcog.2004.04.007
[33]. Dinh Sinh Mai, Long Thanh Ngo, Hung Le Trinh, Hani Hagras: “A hybrid interval type-2 semi-supervised possibilistic fuzzy c-means clustering and particle swarm optimization for satellite image analysis”. Inf. Sci. 548: 398-422 (2021). DOI: https://doi.org/10.1016/j.ins.2020.10.003
[34]. Dinh Sinh Mai, Trong Hop Dang: “An improvement of collaborative fuzzy clustering based on active semi-supervised learning”. FUZZ-IEEE 2022: 1-6, (2022).
[35]. Tran Manh Tuan, Dinh Sinh Mai, Tran Dinh Khang, Phung The Huan, Tran Thi Ngan, Long Giang Nguyen, Vu Duc Thai: “A New Approach for Semi-supervised Fuzzy Clustering with Multiple Fuzzifiers”. Int. J. Fuzzy Syst. 24(8): 3688-3701 (2022). DOI: https://doi.org/10.1007/s40815-022-01363-3
[36]. Abdullah Alghamdi: “A Hybrid Method for Big Data Analysis Using Fuzzy Clustering, Feature Selection and Adaptive Neuro-Fuzzy Inferences System Techniques: Case of Mecca and Medina Hotels in Saudi Arabia”. Arabian Journal for Science and Engineering (2022). DOI: https://doi.org/10.1007/s13369-022-06978-0
