Kernal Based Semi-Supervised Clustering and its Application in Leave Recognition of Bauhinia Blakeana Leaves

Abstract:

A novel kernel based semi-supervised fuzzy clustering algorithm is proposed, and its iterative formula is given. This new algorithm can effectively improve the efficiency of the clustering algorithm. Combined with Fisher projection algorithm, two principal components are extracted from 7 hue statistics and 11 green value statistics, this new semi-supervised clustering method is applied to recognize the angular leaf spot disease of Bauhinia blakeana. The results showed that the consistent rate is 100% for the labeled leaves, and above 95% for other unlabeled leaves.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 3849-3854
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.3849
• Online since: September 2013
• Authors: Xi Yang Yang, Fu Sheng Yu
• Keywords: Angular Spot Disease of Bauhinia Blakeana Leaf, Fisher Projection, Semi-Supervised Fuzzy Clustering

References

[1] Weimin Shi, Xiyang Yang, Zhiwei Li. A Novel Semi Supervised Fuzzy Clustering Algorithm Based on Mahalanobis Distance [J], Journal of Xiamen University, 2012, 51(3): 311-315.

DOI: 10.1109/csss.2012.9

[2] Weimin Shi, Xiyang Yang, Zhiwei Li. Research on Disease Level of Cucumber Downy Mildew Based on Partial Fuzzy C-means Algorithm [J]. Journal of Fujian Teachers University（Natural Science）, 2012, 28(1): 33-37.

[3] Pedrycz, Witold, Waletzky, James. Fuzzy clustering with partial supervision[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 27(5), Oct, 1997: 787-795.

DOI: 10.1109/3477.623232

[4] Abdelhamid Bouchachia, Witold Pedrycz. Enhancement of fuzzy clustering by mechanisms of partial supervision[J]. Fuzzy Sets and Systems, 157(13), 1 July 2006: 1733-1759.

DOI: 10.1016/j.fss.2006.02.015

[5] Timm, H.; Klawonn, F.; Kruse, R. An extension of partially supervised fuzzy cluster analysis[C]. Proc. Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS. 2002, 27-29 June: 63 – 68.

DOI: 10.1109/nafips.2002.1018031

[6] Haitao Gan, Nong Sang, Rui Huang, Xiaojun Tong, Zhiping Dan. Using clustering analysis to improve semi-supervised classification[J]. Neurocomputing, Volume 101, 2013: 290-298.

DOI: 10.1016/j.neucom.2012.08.020

[7] Hong Liu, Shang-teng Huang. Evolutionary semi-supervised fuzzy clustering. Pattern Recognition Letters, 24(16), December 2003: 3105–3113.

DOI: 10.1016/s0167-8655(03)00177-6

[8] Aljoscha Klose, , Rudolf Kruse . Semi-supervised learning in knowledge discovery[J]. Fuzzy Sets and Systems, 149(1), 1 January 2005: 209–233.

DOI: 10.1016/j.fss.2004.07.016

[9] Xuesong Yin, Ting Shu, Qi Huang. Semi-supervised fuzzy clustering with metric learning and entropy regularization Original[J]. Knowledge-Based Systems, Volume 35, November 2012: 304-311.

DOI: 10.1016/j.knosys.2012.05.016

[10] Huaxiang Zhang, Jing Lu. Semi-supervised fuzzy clustering: A kernel-based approach Original Research[J]. Knowledge-Based Systems, 22(6), August 2009: 477-481.

DOI: 10.1016/j.knosys.2009.06.009

[11] Tian Xi-lan, Gasso Gilles, Canu Stéphane. A multiple kernel framework for inductive semi-supervised SVM learning[J]. Neuro-computing, 2012, 90(1): 46-58.

DOI: 10.1016/j.neucom.2011.12.036