Title

Document Clustering - Concepts, Metrics and Algorithms

Journal title

International Journal of Electronics and Telecommunications

Yearbook

2011

Volume

vol. 57

Issue

No 3

Authors

Tarczynski, Tomasz

Divisions of PAS

Nauki Techniczne

Publisher

Polish Academy of Sciences Committee of Electronics and Telecommunications

Date

2011

Identifier

DOI: 10.2478/v10177-011-0036-5 ; eISSN 2300-1933 (since 2013) ; ISSN 2081-8491 (until 2012)

Source

International Journal of Electronics and Telecommunications; 2011; vol. 57; No 3

References

Labrou Y. (1999), Yahoo! as an ontology: using yahoo! categories to describe documents, null, 180. ; Jain A. (1999), Data clustering: a review, ACM Comput. Surv, 31, 264, doi.org/10.1145/331499.331504 ; Cutting D. (1992), Scatter/gather: a cluster-based approach to browsing large document collections, null, 318. ; Salton G. (1975), A vector space model for automatic indexing, Commun. ACM, 18, 613, doi.org/10.1145/361219.361220 ; G. Salton and C. Buckley, "Term weighting approaches in automatic text retrieval," Cornell University, Ithaca, NY, USA, Tech. Rep., 1987. ; Wong S. (1987), On modeling of information retrieval concepts in vector spaces, ACM Trans. Database Syst, 12, 299, doi.org/10.1145/22952.22957 ; Tai X. (2000), Improvement of vector space information retrieval model based on supervised learning, null, 69. ; (1988), Automatic text processing. ; Zhao Y. (2004), Empirical and theoretical comparisons of selected criterion functions for document clustering, Mach. Learn, 55, 311, doi.org/10.1023/B:MACH.0000027785.44527.d6 ; Zeng H. (2004), Learning to cluster web search results, null, 210. ; Olson C. (1995), Parallel algorithms for hierarchical clustering, Parallel Comput, 21. ; C. van Rijsbergen (1979), Information Retrieval. ; Makhoul J. (1999), Performance measures for information extraction, null, 249. ; El-Hamdouchi A. (1989), Comparison of hierarchic agglomerative clustering methods for document retrieval, The Computer Journal, 32, 220, doi.org/10.1093/comjnl/32.3.220 ; M. Steinbach, G. Karypis, and V. Kumar, "A comparison of document clustering techniques," 2000. [Online]. Available: <a target="_blank" href='http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.1505'>http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.1505</a> ; Day W. (1984), Efficient algorithms for agglomerative hierarchical clustering methods, Journal of Classification, 1, 7, doi.org/10.1007/BF01890115 ; Wilkin G. (2008), A practical comparison of two k-means clustering algorithms, BMC Bioinformatics, 9. ; Wu J. (2009), Adapting the right measures for k-means clustering, null, 877. ; Chiang M. (2007), Progress in Artificial Intelligence, 4874, 395, doi.org/10.1007/978-3-540-77002-2_33 ; Arthur D. (2007), k-means++: the advantages of careful seeding, null, 1027. ; Maitra R. (2010), A systematic evaluation of different methods for initializing the k-means clustering algorithm, IEEE Transactions on Knowledge and Data Engineering. ; Milligan G. (1980), The validation of four ultrametric clustering algorithms, Pattern Recognition, 12, 2, 41, doi.org/10.1016/0031-3203(80)90001-1 ; Bradley P. (1998), Refining initial points for k-means clustering, null, 91. ; Mirkin B. (2005), Clustering for Data Mining: A Data Recovery Approach, doi.org/10.1201/9781420034912 ; Fisher D. (1987), Knowledge acquisition via incremental conceptual clustering, Mach. Learn, 2, 139, doi.org/10.1007/BF00114265 ; Cheeseman P. (1996), Menlo Park, CA, USA: American Association for Artificial Intelligence, 153. ; Savaresi S. (2000), Choosing the cluster to split in bisecting divisive clustering algorithms, null. ; Meila M. (2001), An experimental comparison of model-based clustering methods, Mach. Learn, 42, 9, doi.org/10.1023/A:1007648401407 ; Karypis G. (1999), Chameleon: Hierarchical clustering using dynamic modeling, Computer, 32, 68, doi.org/10.1109/2.781637 ; Boley D. (1998), Principal direction divisive partitioning, Data Min. Knowl. Discov, 2, 325, doi.org/10.1023/A:1009740529316 ; Zha H. (2001), Bipartite graph partitioning and data clustering, null, 25. ; Zha C. (2001), Spectral relaxation for k-means clustering, null, 1057. ; Dhillon I. (2001), Concept decompositions for large sparse text data using clustering, Mach. Learn, 42, 143, doi.org/10.1023/A:1007612920971 ; Zamir O. (1997), Fast and intuitive clustering of web documents, null, 287. ; Dash M. (2004), Efficient parallel hierarchical clustering, null. ; Song Y. (2008), Parallel spectral clustering, Machine Learning and Knowledge Discovery in Databases, 374, doi.org/10.1007/978-3-540-87481-2_25 ; Y. Liu, J. Mostafa, and W. Ke, "A fast online clustering algorithm for scatter/gather browsing," 2007. ; Cutting D. (1993), Constant interactiontime scatter/gather browsing of very large document collections, null, 126.