An Ant Colony-based Text Clustering System with Cognitive Situation Dimensions

References

• B.C.M. Fung, K. Wang, M. Ester, Hierarchical document clustering, in: John Wang (Ed.), The Encyclopedia of Data Warehousing and Mining (Idea Group, 2005).
   Google Scholar
• A.K. Jain, R.C. Dubes, Algorithms for Clustering Data, (Prentice Hall, Englewood Cliffs, NJ, 1988).
   Google Scholar
• P. Bradley, U. Fayyad, C. Reina, Clustering very large database using EM mixture models, in: Proceedings of 15th Intern. Conf. on Pattern Recognition (ICPR-2000), 2000, pp.76–80.
   Google Scholar
• K. Nigam, A.K. McCallum, S. Thrun, T.M. Mitchel, Text classification from labeled and unlabeled documents using EM, Machine Learning, 39(2/3) (2000) 103–134.
   Web of Science ®Google Scholar
• T. Hofmann, Probabilistic latent semantic analysis, in: Proceedings of Uncertainty in Artificial Intelligence (UAI'99), 1999, pp.289–296.
   Google Scholar
• M.H.C. Law, M.A.T. Figueiredo, A.K. Jain, Simultaneous feature selection and clustering using mixture models, IEEE Transaction on Pattern Analysis and Machine Intelligence, 26(9) (2004) 1154–1166.
   PubMed Web of Science ®Google Scholar
• E.M. Voorhees, Implementing agglomerative hierarchical clustering algorithms for use in document retrieval, Information Processing and Management, ELSEVIER, 22(6) (1986) 465–476.
   Web of Science ®Google Scholar
• A. El-Hamdouchi, P. Willett, Hierarchical document clustering using ward's method, In: Proceedings of ACM Conference on Research and Development in Information Retrieval, 1986, pp. 149–156.
   Google Scholar
• D.G. Roussinov, H. Chen, Document clustering for electronic meetings: an experimental comparison of two techniques, Decision Support Systems, ELSEVIER, 27(1-2) (1999) 67–79.
   Web of Science ®Google Scholar
• F. Beil, M. Ester, X. Xu, Frequent term-based text clustering, in: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, 2002, pp.436–442.
   Google Scholar
• Y. Li, S.M. Chung, J.D. Holt, Text document clustering based on frequent word meaning sequences, Data & Knowledge Engineering, ELSEVIER, 64(1) (2008) 381–404.
   Web of Science ®Google Scholar
• B.C.M. Fung, K. Wang, M. Ester, Hierarchical document clustering using frequent itemsets, in: Proceedings of SIAM International Conference on Data Mining, 2003, pp.156–162.
   Google Scholar
• B. Choudhary, P. Bhattacharyya, Text clustering using semantics, in: Proceedings of the 11th International WorldWide Web Conference, 2002, pp.326–332.
   Google Scholar
• J. Sedding, D. Kazakov, WordNet-based text document clustering, in: Proceedings of COLING-2004 Workshop on Robust Methods in Analysis of Natural Language Data, 2004, pp.242–247.
   Google Scholar
• A. Hotho, S. Staab, G. Stumme, Ontologies improve text document clustering, in: Proceedings of the 3rd IEEE International Conference on Data Mining, 2003, pp.541–544.
   Google Scholar
• R. Saraçoğlu, K. Tütüncü, N. Allahverdi, A fuzzy clustering approach for finding similar documents using a novel similarity measure, Expert Systems with Applications, ELSEVIER, 33(3) (2007) 600–605.
   Web of Science ®Google Scholar
• W. Tjhi, L. Chen, Possibilistic fuzzy co-clustering of large document collections, Pattern Recognition, ELSEVIER, 40(12) (2007) 3452–3466.
   Google Scholar
• Y. Liu, X. Wang, C. Wu, ConSOM: A conceptional self-organizing map model for text clustering, Neurocomputing, ELSEVIER, 71(4-6) (2008) 857–862.
   Web of Science ®Google Scholar
• L. Jing, M. K. Ng, J. Z. Huang, Knowledge-based vector space model for text clustering, Knowledge Information Systems, 25 (2010) 35–55.
   Web of Science ®Google Scholar
• T.A. van Dijk, W. Kintsch,Strategies of discourse comprehension, (Academic Press, 1983).
   Google Scholar
• R.A. Zwaan, J.P. Magliano, A.C. Graesser, Dimensions of situation model construction in narrative comprehension, Journal of Experimental Psychology, Learning, Memory, and Cognition, American Psychological Association, 21 (1995) 386–397.
   Web of Science ®Google Scholar
• P.N. Johnson-Laird,Mental models: Towards a cognitive science of language, inference, and consciousness, (Cambridge, MA: Harvard University Press, 1983).
   Google Scholar
• M. Srinivas and L. M. Patnik,Genetic algorithms: A survey, (Los lamitos: IEEE Computer Society Press, 1994).
   Google Scholar
• M. Dorigo, G. Di Caro, The ant colony optimization meta-heuristic. In D. Corne, M. Dorigo, and F. Glover, (Eds.), New ideas in optimization, London: McGraw-Hill, 1999, pp.11–32.
   Google Scholar
• M. Dorigo, V. Maniezzo, A. Colorni, The ant system: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics±Part B, 26(1) (1996) 29–41.
   PubMed Web of Science ®Google Scholar
• T. Stützle, M. Dorigo, The ant colony optimization metaheuristic: algorithms, applications, and advances, In F. Glover and G. Kochenberger, (Eds.), Handbook of metaheuristics (Norwell, MA: Kluwer Academic Publishers, 2003), pp.251–285.
   Google Scholar
• M. H. Aghdam, N. Ghasem-Aghaee, M. E. Basiri, Text feature selection using ant colony optimization, Expert Systems with Applications, ELSEVIER, 36 (2009) 6843±6853.
   Web of Science ®Google Scholar
• R.A. Zwaan, G.A. Radvansky, Situation Models in Language Comprehension and Memory, Psychological Bulletin, American Psychological Association, 123(2) (1998) 162–185.
   PubMed Web of Science ®Google Scholar
• W. Schneider, J. Körkel, The knowledge base and text recall: Evidence from a short-term longitudinal study, Contemporary Educational Psychology, 14 (1989) 382–393.
   Web of Science ®Google Scholar
• Y. Guo, Z. Shao, N. Hua, A Cognitive Interactionist Sentence Parser with Simple Recurrent Networks, Information Sciences, ELSEVIER, 180(23) (2010) 4695–4705.
   Web of Science ®Google Scholar
• G.A. Miller, R. Beckwith, C. Fellbaum, D Gross, K.J. Miller, Introduction to WordNet: An on-line lexical database, International Journal of Lexicography, 3(4) (1990) 235–312.
   Google Scholar
• Y. Guo, Z. Shao, N. Hua, Automatic text categorization based on content analysis with cognitive situation models, Information Sciences, ELSEVIER, 180(5) (2010) 613–630.
   Web of Science ®Google Scholar
• Reuters-21578, Distribution 1.0, http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html. (2003).
   Google Scholar
• D.D. Lewis, Y. Yang, T. Rose, F. Li, RCV1: A New Benchmark Collection for Text Categorization Research, Journal of Machine Learning Research, 5 (2004) 361–397.
   Web of Science ®Google Scholar
• S. Zhong, J. Ghosh, Generative Model-based Document Clustering: a comparative study, Knowledge and Information Systems, SPRINGER, London Ltd., 8(3) (2005) 374–384. (doi:10.1007/s10115-004-0194-1)
   Web of Science ®Google Scholar
• A. Strehl, J. Ghosh, R.J. Mooney, Impact of similarity measures on web-page clustering, In AAAI Workshop on AI for Web Search, 2000, pp.58–64.
   Google Scholar
• A. Strehl, J. Ghosh, Cluster ensembles - a knowledge reuse framework for combining partitions, Journal of Machine Learning Research, MIT Press, 3 (2002) 583–617.
   Google Scholar
• G. Hirst, D. St-Onge, Lexical chains as representations of context for the detection and correction of malapropisms, (Fellbaum, 1998), pp.305–332.
   Google Scholar
• X.Z. Wang, L.C. Dong, J.H. Yan, Maximum ambiguity based sample selection in fuzzy decision tree induction, IEEETransactions on Knowledge and Data Engineering, IEEE Computer Society, (2011) 292–300. (doi:10.1109/TKDE.2011.67)
   Web of Science ®Google Scholar