Application of a soft competition learning method in document clustering

doi:10.19682/j.cnki.1005-8885.2018.0012

JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM ›› 2018, Vol. 25 ›› Issue (3): 80-91.doi: 10.19682/j.cnki.1005-8885.2018.0012

• Others • Previous Articles Next Articles

Application of a soft competition learning method in document clustering

yehang zhu ²

Received:2017-08-08 Revised:2018-01-16 Online:2018-06-29 Published:2018-06-30
Contact: yehang zhu E-mail:zhuyehang@126.com
Supported by:
The Project of Natural Science Foundation research project of Shaanxi Province of China;The humanities and social sciences research youth fund project of Ministry of Education of China

Abstract

Abstract: Hard competition learning has the feature that each point modifies only one cluster centroid that wins. Correspondingly, soft competition learning has the feature that each point modifies not only the cluster centroid that wins, but also many other cluster centroids near this point. A soft competition learning method is proposed. Centroid all rank distance(CARD), CARDx, and Centroid all rank distance batch K-means(CARDBK) are three clustering algorithms that adopt the soft competition learning method proposed by us. Among them the extent to which one point affects a cluster centroid depends on the distances from this point to the other nearer cluster centroids, rather than just the rank number of the distance from this point to this cluster centroid among the distances from this point to all cluster centroids. In addition, the validation experiments are carried out in order to compare the three soft competition learning algorithms CARD, CARDx, and CARDBK with several hard competition learning algorithms as well as neural gas(NG) algorithm on five data sets from different sources. Judging from the values of five performance indexes in the clustering results, this kind of soft competition learning method has better clustering effect and efficiency, and has linear scalability.

Key words: clustering methods, text processing, document handling, competition learning method

CLC Number:

TP391

yehang zhu. Application of a soft competition learning method in document clustering[J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2018, 25(3): 80-91.

References

1. Zhu Y H. Research on document clustering algorithm. Ph D Thesis. Xi’an, China: Northwestern Polytechnical University, 2009 (in Chinese)

2. Zhu Y H, Li Y L, Yang X W. Improved clustering algorithm CARD based on Neural-Gas algorithm. Application Research of Computers, 2015, 32(5): 1309-1312 (in Chinese)

3. Zhu Y H, Li Y L, Cui M T, et al. Clustering algorithm CARDBK improved from k-means algorithm. Computer Science, 2015, 42(3): 201-205 (in Chinese).

4. Johnson T, Singh S K. Divisive hierarchical bisecting min-max clustering algorithm. Advances in Intelligent Systems and Computing: Proceedings of the International Conference on Data Engineering and Communication Technology (ICDECT’16): Vol 1, Mar 10-11, 2016, Pune, India. AISC 468. Singapore: Springer-Verlag, 2017: 579-592

5. Bishnu P S, Bhattacherjee V. Software cost estimation based on modified k-modes clustering Algorithm. Natural Computing, 2016, 15(3): 415-422

6. El Aziz M A, Selim I M, Essam A. Open cluster membership probability based on k-means clustering algorithm. Experimental Astronomy, 2016, 42(1): 49-59

7. Huang C Q, Chung F l, Wang S T. Generalized competitive agglomeration clustering algorithm. International Journal of Machine Learning and Cybernetics, 2016: 1-25

8. Li W J, Feng Y M, Li D J, et al. Micro-blog topic detection method based on BTM topic model and k-means clustering algorithm. Automatic Control and Computer Sciences, 2016, 50(4): 271-277

9. Huang Y, Zhang W Z, Zhang H Y, et al (2016) A fast clustering algorithm for massive short message. Human Centered Computing: Proceedings of the 2nd International Conference on Human Centered Computing (HCC’16), Jan 7-9, 2016, Colombo, Sri Lanka. LNCS 9567. Cham, Switzerland: Springer-Verlag, 2016: 183-192

10. Zhang B, Nie T Z, Shen D R, et al. A graph clustering algorithm for citation networks. Web Technologies and Applications: Proceedings of the 18th Asia-Pacific Web Conference (APWeb’16), Sept 23-25, 2016, Suzhou, China. LNCS 9932. Cham, Switzerland: Springer-Verlag, 2016: 414-418

11. Modha S, Joshi K. Performance analysis of clustering algorithm in sensing microblog for smart cities. Advances in Intelligent Systems and Computing: Proceedings of the International Congress on Information and Communication Technology (ICICT’15): Vol 2, Oct 9-10, 2015, Udaipur, India. AISC 439. Singapore: Springer-Verlag, 2015: 467-475

12. Zhong S. Efficient online spherical k-means clustering. Proceedings of the 2005 IEEE International Joint Conference on Neural Networks (IJCNN’05): Vol 5, Jul 31-Aut 4, 2005, Montreal, Canada. Piscataway, NJ, USA: IEEE, 2005: 3180-3185

13. Huo J Y, Zhang H L. An improved k-means clustering algorithm based on the Voronoi diagram method. Advances in Swarm Intelligence: Proceedings of the 7th International Conference on Swarm Intelligence (ICSI’16) Vol 2, Jun 25-30, 2016, Bali, Indonesia. LNCS 9713. Cham, Switzerland: Springer-Verlag, 2016: 107-114

14. Nagaraju S, Kashyap M, Bhattachraya M. An effective density based approach to detect complex data clusters using notion of neighborhood difference. International Journal of Automation and Computing, 2017, 14(1): 57-67

15. Wang Z T, Kang P, Wu Z W, et al. A density-based clustering algorithm with educational applications. Current Developments in Web Based Learning: Proceedings of the 14th International Conference of Web Based Learning (ICWL’15), Nov 5-8, Guangzhou, China. LNCS 9584. Cham, Switzerland: Springer-Verlag, 2016: 118-127

16. Dey L, Ranjan K, Verma I, et al. Semantic overlapping clustering algorithm for analyzing short-texts. Rough Sets: Proceedings of the International Joint Conference on Rough Sets (IJCRS’16), Oct 7-11, 2016, Santiago, Chile. LNCS 9920. Cham, Switzerland: Springer-Verlag, 2016: 470-479

17. Li Y J, Luo C N, Chung S M. A parallel text document clustering algorithm based on neighbors. Cluster Computing, 2015, 18(2): 933-948

18. Xu D G, Zhao P L, Yang C H, et al. A novel Minkowski-distance-based consensus clustering algorithm. International Journal of Automation and Computing, 2017, 14(1): 33-44

19. Hilala J, Rovshan A. Applying k-means clustering algorithm using oracle data mining to banking data. Advances in Intelligent Systems and Computing: Proceedings of the 9th International Conference on Management Science and Engineering Management (ICMSEM’15), Jul 21-25, 2015, Karlsruhe, Germany. AISC 362. Berlin, Germany: Springer-Verlag, 2015: 809-816

20. Judith J E, Jayakumari J. Optimized distributed text document clustering algorithm. Advances in Intelligent Systems and Computing: Proceedings of the 2014 International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (ICAEES’14): Vol 2, Apr 22-23, 2014, Kumaracoil, India. AISC 325. New Delhi, India: Springer-Verlag, 2015: 565-574

21. Liu G L, Tan Y Q, Yu L M, et al. Application research of modified k-means clustering algorithm. Proceedings of the 19th International Conference on Industrial Engineering and Engineering Management (IE&EM’12), Oct 27-29, 2012, Changsha, China. Berlin, Germany: Springer-Verlag, 2013: 1269-1280

Metrics

Comments

Copyright © 2020 The Journal of China Universities of Posts and Telecommunications
　 Adress: P.O. Box 231,Beijing University of Posts and Telecommunications,10 Xi Tucheng Road,Beijing 100876,P.R.China　Post Code: 100081
Tel：86-010-62282493　Fax： 86-010-62283461　E-mail: jchupt@bupt.edu.cn
Support by: Beijing Magtech Co.Ltd

[1]	Guo Xiangbo, Wang Jian, Huang Mengjie, Wang Minghui, Yang Jian, Yu Yongtao. Deep knowledge tracking algorithm based on forgetting law [J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(1): 17-27.
[2]	Wang Xianlun, Wang Guangyu, Cui Yuxia. Facial expression recognition based on improved ResNet [J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(1): 28-38.
[3]	Wu Hongxin, Lin Zhijian, Chen Pingping, Chen Feng. Joint partial computation offloading and resource allocation in MEC-enable networks [J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(1): 80-86.
[4]	Kong Chao, Ou Weihua, Gong Xiaofeng, Li Weian, Han Jie, Yao Yi, Xiong Jiahao. Face anti-spoofing based on multi-modal and multi-scale features fusion [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(6): 73-82.
[5]	Zhang Han Jing Yinji Zhao Yongli. FS-LSTM: sales forecasting in e-commerce on feature selection [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(5): 92-98.
[6]	Shi Jinjing, Wang Wenxuan, Xiao Zimeng, Mu Shuai, Li Qin. Quantum classifier with parameterized quantum circuit based on the isolated quantum system [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(4): 21-31.
[7]	Liu Hailing, Zhang Jie, Qin Sujuan, Gao Fei. Quantum algorithm for soft margin support vector machine with hinge loss function [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(4): 32-41.
[8]	Wang Jian, Qiao Kuoyuan, Yuan Yanlei, Liu Xiaole, Yang Jian. Adaptive learning path recommendation model for examination-oriented education [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(4): 77-88.
[9]	Meng Wei, Wang Liting, Lu Meng. Summary of research on recommendation system based on serendipity [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(4): 89-105.
[10]	Jia Wei, Gong Chao. Precise and efficient Chinese license plate recognition in the real monitoring scene of intelligent transportation system [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(3): 1-14.
[11]	Song Yue, Wu Chengmao, Tian Xiaoping, Song Qiuyu. Enhanced kernel-based fuzzy local information clustering integrating neighborhood membership [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(6): 65-81.
[12]	Xue Chenzi, Wei Yifei, Zhang Yong. Performance optimization for smart grid blockchain integrated with fog computing using DDQN [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(2): 68-78.
[13]	Guo Hairu, Meng Xueyao, Liu Yongli, Liu Shen. Improved HHO algorithm based on good point set and nonlinear convergence formula [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(2): 48-67.
[14]	Wu Chengmao, Cao Zhuo. Entropy-like distance driven fuzzy clustering with local information constraints for image segmentation [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(1): 24-40.
[15]	Shan Rui, Jiang Lin, Deng Junyong, Cui Pengfei, Zhang Yuting, Wu Haoyue, Xie Xiaoyan. Parallel design of convolutional neural networks for remote sensing images object recognition based on data-driven array processor [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(6): 87-100.

Application of a soft competition learning method in document clustering

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments