A Text Clustering Approach of Chinese News Based on Neural Network Language Model

International Journal of Parallel Programming

Volumn 44, Issue 1, 2016, Pages 198-206

  Fan, Zhaoxin ^a   Chen, Shuoying ^a   Zha, Li ^b   Yang, Jiadong ^c  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^c Sohu Inc   (China)

Author keywords

Chinese news; Data mining; Fuzzy k means; Language model

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; COMPLEX NETWORKS; COMPUTATIONAL LINGUISTICS; DATA MINING; LEARNING SYSTEMS;

CHINESE NEWS; CLUSTERING TECHNOLOGIES; FUZZY K-MEANS; KEYWORD-CLUSTERING; LANGUAGE MODEL; NETWORK LANGUAGE; TEXT CLUSTERING; WORD CLUSTERING;

CLUSTER ANALYSIS;

EID: 84953835015     PISSN: 08857458     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10766-014-0329-2     Document Type: Article

References (19)

1. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
2. Aggarwal, C.C., Zhai, C.X.: A survey of text clustering algorithms. Mining text data. Springer, US (2012)
3. Ko, Y.: A study of term weighting schemes using class information for text classification. In: Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval. ACM (2012)
4. Bengio, Y., et al.: Neural probabilistic language models. Innovations in machine learning. Springer, Berlin (2006)
5. Berkhin, P.: A survey of clustering data mining techniques. Grouping multidimensional data. Springer, Berlin (2006)
6. Rajaraman, A., Ullman, J.D.: Data mining. Mining of massive datasets. Cambridge University Press, Cambridge (2012)
7. Casella, G., George, E.I.: Explaining the Gibbs sampler. Am. Stat. 46.3, 167–174 (1992)
8. Zeng, H.-J., et al.: CBC: clustering based text classification requiring minimal labeled data. Data mining, 2003. ICDM 2003. In: 3rd IEEE international conference on IEEE (2003)
9. Decherchi, S., et al.: A text clustering framework for information retrieval. J. Inf. Assur. Sec. 4, 174–182 (2009)
10. Kalogeratos, A., Likas, A.: Text document clustering using global term context vectors. Knowl. Inf. Syst. 31.3, 455–474 (2012)
11. Kang, S.-S.: Keyword-based document clustering. In: Proceedings of the 6th international workshop on information retrieval with Asian languages-Volume 11. Association for computational linguistics (2003)
12. Cheng, H.-C., Chiun-Chieh, H.S.U.: Using topic keyword clusters for automatic document clustering. IEICE Trans. Inf. Syst. 88.8, 1852–1860 (2005)
13. Jing, L., Ng, M.K., Huang, J.Z.: An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. Knowl. Data Eng. IEEE Trans. 19.8, 1026–1041 (2007)
14. Berry, M.W., Castellanos, M. (eds.): Survey of text mining. Springer, New York (2004)
15. Hotho, A., Nurnberger, A., Paaß G.: A brief survey of text mining. Ldv Forum. 20, 19–62 (2005)
16. Horng, Y.-J.: A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. Fuzzy Syst. IEEE Trans. 13.2, 216–228 (2005)
17. Tjhi, W.-C., Chen, L.: A heuristic-based fuzzy co-clustering algorithm for categorization of high-dimensional data. Fuzzy Sets Syst. 159.4, 371–389 (2008)
18. Heinrich, G.: Parameter estimation for text analysis. Technical report (2005)
19. Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2.1, 37–63 (2011)