Weighted FCM Clustering Algorithm Based on Jeffrey Divergence Similarity Measure

Chenwen Wu; Ning Ma; Yufan Jiang

doi:10.3788/LOP202158.0810006

[1] Zhang J, Fu J P, Li X H. Low-rank regularized heterogeneous tensor decomposition algorithm for subspace clustering[J]. Laser & Optoelectronics Progress, 55, 071003(2018).

[2] Pei X B, Wu T, Chen C B. Automated graph regularized projective nonnegative matrix factorization for document clustering[J]. IEEE Transactions on Cybernetics, 44, 1821-1831(2014). http://europepmc.org/abstract/med/25222725

[3] Tong X J, Wu Y C. An improved spectral ensemble clustering algorithm in data mining[J]. Journal of Terahertz Science and Electronic Information Technology, 18, 497-503(2020).

[4] Wang Q, Wang C, Feng Z Y et al. Review of K-means clustering algorithm[J]. Electronic Design Engineering, 20, 21-24(2012).

[5] Hathaway R J, Bezdek J C, Hu Y K. Generalized fuzzy c-means clustering strategies using Lp norm distances[J]. IEEE Transactions on Fuzzy Systems, 8, 576-582(2000). http://ieeexplore.ieee.org/document/873580

[6] Zhou S G, Zhou A Y, Cao J. A data-partitioning-based DBSCAN algorithm[J]. Journal of Computer Research and Development, 37, 1153-1159(2000).

[7] Chakraborty S, Das S. K-means clustering with a new divergence-based distance metric: convergence and performance analysis[J]. Pattern Recognition Letters, 100, 67-73(2017). http://www.sciencedirect.com/science/article/pii/S0167865517303380

[8] Banerjee A, Merugu S, Dhillon I et al. Clustering with bregman divergences[C]. //Proceedings of the 2004 SIAM International Conference on Data Mining, Philadelphia, PA: Society for Industrial and Applied Mathematics(2004).

[9] Zhu Z L, Wang J F. Image segmentation based on adaptive fuzzy C-means and post processing correction[J]. Laser & Optoelectronics Progress, 55, 011004(2018).

[10] Puzicha J, Hofmann T, Buhmann J M. Non-parametric similarity measures for unsupervised texture segmentation and image retrieval[C]. //Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 17-19, 1997, San Juan, Puerto Rico, USA, 267-272(1997).

[11] Wang X Z, Wang Y D, Wang L J. Improving fuzzy c-means clustering based on feature-weight learning[J]. Pattern Recognition Letters, 25, 1123-1132(2004). http://doi.ieeecomputersociety.org/10.1109/PACIIA.2008.153

[12] Hung W L, Yang M S, Chen D H. Bootstrapping approach to feature-weight selection in fuzzy c-means algorithms with an application in color image segmentation[J]. Pattern Recognition Letters, 29, 1317-1325(2008). http://dl.acm.org/doi/10.1016/j.patrec.2008.02.003

[13] Xing H J, Wang X Z, Ha M H. A comparative experimental study of feature-weight learning approaches[C]. //2011 IEEE International Conference on Systems, Man, and Cybernetics, October 9-12, 2011, Anchorage, AK, USA, 3500-3505(2011).

[14] Nazari M, Shanbehzadeh J, Sarrafzadeh A. Fuzzy c-means based on automated variable feature weighting[C]. //Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2013, March 13 - 15, 2013, Hong Kong.

[15] Ferreira M R P, de Carvalho F D A T. Kernel fuzzy c-means with automatic variable weighting[J]. Fuzzy Sets and Systems, 237, 1-46(2014).

[16] Saha A, Das S. Geometric divergence based fuzzy clustering with strong resilience to noise features[J]. Pattern Recognition Letters, 79, 60-67(2016). http://www.sciencedirect.com/science/article/pii/S0167865516300654

[17] Lin J X, Wu L P, Wu J W et al. Adaptive FCM clustering algorithm based on sample and feature weights[J]. Journal of Natural Science of Heilongjiang University, 35, 244-252(2018).

[18] Zhou Z P, Zhu S W. Kernel-based multiobjective clustering algorithm with automatic attribute weighting[J]. Soft Computing, 22, 3685-3709(2018). http://smartsearch.nstl.gov.cn/paper_detail.html?id=d1b5c3df109f55acd0d6383b0a085294

[19] Zhao Z M, Zhu Z L, Liu Y J et al. Fuzzy C-means clustering algorithm for image segmentation insensitive to cluster size[J]. Laser & Optoelectronics Progress, 57, 021001(2020).

[20] Dunn J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Journal of Cybernetics, 3, 32-57(1973). http://www.ams.org/mathscinet-getitem?mr=375857

[21] Bezdek J C. Pattern recognition with fuzzy objective function algorithms[M]. Boston: Springer, 442, 1-13(1983).

[22] Seal A, Karlekar A, Krejcar O et al. Fuzzy c-means clustering using Jeffreys-divergence based similarity measure[J]. Applied Soft Computing, 88, 106016(2020). http://www.sciencedirect.com/science/article/pii/S1568494619307987

[23] Wei X, Tang X H. Advanced dimension power FCM arithmetic with genetics[J]. Journal of Honghe University, 5, 39-42(2007).

[24] Zheng J, You H J. Change detection with SAR images based on radon transform and Jeffrey divergence[J]. Journal of Radars, 1, 182-189(2012).

[25] Groll L, Jakel J. A new convergence proof of fuzzy c-means[J]. IEEE Transactions on Fuzzy Systems, 13, 717-720(2005). http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1516160

[26] Chen Y W, Shen L L, Zhong C M et al. Survey on density peak clustering algorithm[J]. Journal of Computer Research and Development, 57, 378-394(2020).

[27] Shang F H, Jiao L C, Shi J R et al. Fast affinity propagation clustering: a multilevel approach[J]. Pattern Recognition, 45, 474-486(2012).

[28] Vinh N X, Epps J, Bailey J. Bibliometrics: information theoretic measures for clusterings comparison[C]. //The International Conference on Machine Learning, July 11-14, 2010, Qingdao, China. New York: ACM, 2837-2854(2010).

[29] Clausius R. Ueber eine veränderte Form des zweiten Hauptsatzes der mechanischen Wärmetheorie[J]. Annalen Der Physik Und Chemie, 169, 481-506(1854).