W-kmeans: Clustering news articles using WordNet

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6278 LNAI, Issue PART 3, 2010, Pages 379-388

  Bouras, Christos ^a,b   Tsogkas, Vassilis ^a  

^a UNIVERSITY OF PATRAS   (Greece)

^b University of Patras and Computer Technology Institute   (Greece)

Author keywords

Cluster Labeling; k means; News clustering; Partitional Clustering

Indexed keywords

BAG OF WORDS; CLUSTER LABELING; CLUSTERING PROCESS; DOCUMENT CLUSTERING; EXTERNAL KNOWLEDGE; HIGH QUALITY; HYPERNYMS; INFORMATION KERNELS; K-MEANS; K-MEANS ALGORITHM; NEWS ARTICLES; NEWS CLUSTERING; PARTITIONAL CLUSTERING; WORDNET;

CLUSTERING ALGORITHMS; CONCENTRATION (PROCESS); KNOWLEDGE BASED SYSTEMS; ONTOLOGY;

SEMANTIC WEB;

EID: 78449248430     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15393-8_43     Document Type: Conference Paper

References (13)

1. Zhao., Y., Karypi, G.: Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering. Machine Learning 55(3), 311-331 (2004)
2. Yanjun, L., Soon, C.: Parallel bisecting k-means with prediction clustering algorithm. The Journal of Supercomputing 39, 19-37 (2007)
3. Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proc. of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pp. 1027-1035 (2007)
4. Varelas, G., Voutsakis, E., Raftopoulou, P., Petrakis, E., Milios, E.: Semantic similarity methods in wordNet and their application to information retrieval on the web. In: Workshop On Web Information And Data Management, Proceedings of the 7th annual ACM international workshop on Web information and data management, pp. 10-16 (2005)
5. Chen, C.-L., Frank, S., Tseng, C., Liang, T.: An integration of fuzzy association rules and wordNet for document clustering. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 147-159. Springer, Heidelberg (2009)
6. Carmel, D., Roitman, H., Zwerdling, N.: Enhancing cluster labeling using wikipedia. In: Proceedings of the 32nd international ACM SIGIR conference on Research and development ininformation retrieval, pp. 139-146 (2009)
7. Sedding, J., Kazakov, D.: WordNet-based text document clustering. In: Proc. of COLING-Workshop on Robust Methods in Analysis of Natural Language Data (2004)
8. Treeratpituk, P., Callan, J.: Automatically labeling hierarchical clusters. In: Proceedings of the 2006 international conference on Digital government research, San Diego, California, May 21-24 (2006)
9. Tseng, Y.H.: Generic title labeling for clustered documents. In: Expert Systems With Applications, vol. 37(3), pp. 2247-2254. Elsevier, Amsterdam (2009)
10. Bouras, C., Poulopoulos, V., Tsogkas, V.: PeRSSonal's core functionality evaluation: Enhancing text labeling through personalized summaries. Data and Knowledge Engineering Journal, Elsevier Science 64(1), 330-345 (2008)
11. Bouras, C., Tsogkas, V.: Improving text summarization using noun retrieval techniques. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 593-600. Springer, Heidelberg (2008)
12. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications of the ACM 18(11), 613-620 (1975)
13. Taeho, J., Malrey, L.: The Evaluation Measure of Text Clustering for the Variable Number of Clusters. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4492, pp. 871-879. Springer, Heidelberg (2007)