138

REFERENCES

[1] R. Baeza-Yates, B. Ribeiro-Neto, Modern Information Retrieval,Addison-Wesley, 1999.

[2] P.P. Bonissone, “Soft Computing: The Convergence of EmergingReasoning Technologies”, Soft Computing, vol.1, no.1, pp.6–18,1997.

[3] H. Chen, “Introduction to The Special Issue on Web Retrieval andMining: A Machine Learning Perspective”, Journal of the AmericanSociety for Information Science and Technology, vol.54, no.7,pp.621–624, 2003.

[4] F. Crestani, G. Pasi (Eds.), Soft Computing in Information Retrieval,Studies in Fuzziness and Soft Computing Series, vol. 50, Physica-Verlag, 2000.

[5] M. Nikravesh, V. Loia, B. Azvine, “Fuzzy Logic and the Internet(FLINT): Internet, World Wide Web and Search Engines”, SoftComputing , vol.6, no.5, pp.287–299, 2002.

[6] G. Salton, M.J. McGill, Introduction to Modern Information Retrieval,McGraw-Hill, 1989.

[7] L.A. Zadeh, “Fuzzy Logic, Neural Networks and Soft Computing”,Communications of the ACM , vol.37, no.3, pp. 77–84, 1994.

[8] L.A. Zadeh, “What is Soft Computing?”, Soft Computing , vol.1,pp.1-1, 1997.

[9] C.J.Van Rijsbergen, Information Retrieval, second edition,Butterworth, 1979.

[10]A.Bookstein, “Outline of a General Probabilistic Retrieval Model”,Journal of Documentation, vol.39, Issue 2, pp.63-72, 1983.

[11]N.Fuhr, “Probabilistic Models in Information Retrieval”, ComputerJournal, vol.35, Issue 3, pp. 243-255, 1992.

[12]G.Bordogna, P.Carrara, G.pasi, “Fuzzy Approaches to ExtendBoolean Information Retrieval”, Fuzziness in database managementsystems, pp.231-274, 1995.

[13]M. Gordon, “Probabilistic and Genetic Algorithms for DocumentRetrieval”, Communications of the ACM, vol.31, No.10, pp.1208–1218, 1988.

[14]D. Vrajitoru, “Crossover Improvement for the Genetic Algorithm inInformation Retrieval”, Information Processing and Management,vol.34, No.4, pp. 405–415, 1998.

[15]W. Fan, M.D. Gordon, P. Pathak, “Personalization of SearchEngine Services for Effective Retrieval and KnowledgeManagement”, Proc. 2000 International Conference on InformationSystems (ICIS), Brisbane, Australia, 2000.

[16]A.M. Robertson, P. Willet, “Generation of Equifrequent Groups ofWords Using a Genetic Algorithm”, Journal of Documentation,vol.50, No.3, pp. 213–232, 1994.

139

[17]M. Gordon, “User-Based Document Clustering by RedescribingSubject Description with a Genetic Algorithm”, Journal of theAmerican Society for Information Science, vol.42, No.5, pp.311–322,1991.

[18]C.J.Van Rijsbergen, Information Retrieval, second ed. Butterworth,1979.

[19]R.L. Walker, “Search Engine Case Study: Searching the WebUsing Genetic Programming and MPI”, Parallel Computing, vol.27,pp.71–89, 2001.

[20]Robertson, P. Willet, “An Upperbound to the Performance forRanked-Output Searching: Optimal Weighting of Query TermsUsing a Genetic Algorithm”, Journal of Documentation, vol.52,No.4,pp. 405–420, 1996.

[21]J. Horng, C. Yeh, “Applying Genetic Algorithms to QueryOptimization in Document Retrieval”, Information Processing andManagement, vol.36, 737-759, 2000.

[22]C.Lopez-Pujalte, V. Guerrero, F. Moya, “A Test of GeneticAlgorithms in Relevance Feedback”, Information Processing &Management 38 (2002), pp. 793–805, 2002.

[23]López-Pujalte, C., Guerrero-Bote, V. P., and de Moya-Anegón, F,“Genetic Algorithms In Relevance Feedback: A Second Test andNew Contributions”, Information Processing and Management ,vol.39, No.5, pp. 669-807, 2003.

[24]C.Lopez-Pujalte, V.Guerrero, F.Moya, “Order-Based FitnessFunctions for Genetic Algorithms Applied to RF”, Journal of theAmerican Society for Information science and Technology , vol.54,No.2, pp. 152-160, 2003.

[25]M.P. Smith, M. Smith, “The Use of Genetic Programming to BuildBoolean Queries for Text Retrieval Through Relevance Feedback”,Journal of Information Science, vol.23, No.6, pp. 423–431, 1997.

[26]C.M. Fonseca, P.J. Fleming, “Genetic Algorithms for MultiobjectiveOptimization: Formulation, Discussion and Generalization”, Proc.Fifth International Conference on Genetic Algorithms, pp. 416–423,2003.

[27]D.H. Kraft, F.E.Petry, B.P.Buckes, T.Sadasivan, “GeneticAlgorithm for Query Optimization in Information Retrieval:Relevance Feedback”, Genetic Algorithms and Fuzzy Logic Systems,pp.155–173, 1997.

[28]O. Cordon, F. Moya, C. Zarco, “AGA-P Algorithm to AutomaticallyFormulate Extended Boolean Queries for a Fuzzy InformationRetrieval System”, Mathware & Soft Computing , vol.7, No.2–3, pp.309–322, 2000.

[29]O. Cordon, F. Moya, C. Zarco, “A New Evolutionary AlgorithmCombining Simulated Annealing and Genetic Programming forRelevance Feedback”, Soft Computing , vol.6, pp.308 – 319, 2002.

140

[30]O. Cordon, E. Herrera-Viedma, M. Luque, F. Moya, C. Zarco,“Analyzing the Performance of a Multiobjective GA-P Algorithm forLearning Fuzzy Queries in a Machine Learning Environment”,International Fuzzy Systems Association World Congress, LNAI2715, Istanbul, Turkey, 2003.

[31]O. Cordon, F. Moya, C. Zarco, “Automatic Learning of MultipleExtended Boolean Queries by Multiobjective GA-P Algorithms”, in:V. Loia, M. Nikravesh, L.A. Zadeh (Eds.), Fuzzy Logic and theInternet, Springer, 2003.

[32]M.Boughanem, “On using GA in IR”, Journal of the AmericanSociety for Information Science and Technology , vol.53, No.11, pp.934-942, 2002.

[33]M.Boughanem, “Multiple Query Evaluation Based on EnhancedGA”, Information Processing and Management ,vol.39, pp. 215-231, 2003.

[34]S. Cho, J. Lee, “A Human-Oriented Image Retrieval System UsingInteractive Genetic Algorithm”, IEEE Transactions on System, Manand Cybernetics. Part A: Systems and Humans, vol.32, No.3, pp.452–458, 2002.

[35]S. Kato, S. Iisaku, “An Image Retrieval Method Based on a GeneticAlgorithm”, Proc. Twelfth International conference on InformationNetworking (ICOIN-12), pp. 333–336, 1998.

[36]Z. Stejic, Y. Takama, K. Hirota, “Genetic Algorithm-BasedRelevance Feedback for Image Retrieval Using Local SimilarityPatterns”, Information Processing & Management , vol.39, 2003.

[37]Larsen, N. Martın, M.J. Martın-Bautista, M.A. Vila, “Using GeneticFeature Selection for Optimizing User Profile”, Mathware & SoftComputing 7, pp. 275–286, 2000.

[38]V. Loia, P. Luengo, “An Evolutionary Approach to Automatic WebPage Categorization and Updating”, First Asia–Pacific Conference,Maebashi City, Japan, pp. 292–302, 2001.

[39]H.Chen, “An Intelligent Personal Spider, for DynamicInternet/Intranet”, Decision Support Systems, vol.23, pp.41-58,1998.

[40]J. Yang, R. Korfhage, “Query Modifications Using GeneticAlgorithms in Vector Space Models”, International Journal of ExpertSystems , vol.7, No.2, pp.165–191, 1994.

[41]O. Cordon, E. Herrera-Viedma, M. Luque, “Evolutionary Learningof Boolean Queries by Multiobjective Genetic Programming”, Proc.PPSN-VII, Granada, Spain, LNCS 2439,pp. 710–719, 2002.

[42]W. Fan, M. Gordon, P. Pathak, “Automatic Generation of aMatching Function By Genetic Programming for EffectiveInformation Retrieval”, America’s Conference on informationSystem, Milwaukee, USA, August 1999.

141

[43]W. Fan, M.D. Gordon, P. Pathak, “Discovery of Context-SpecificRanking Functions for Effective Information Retrieval UsingGenetic Programming”, IEEE Transactions on Knowledge and DataEngineering, vol.16, Issue 4 , pp. 523 – 527, April 2004.

[44]Gloria Bordogna , Gabriella Pasi , R.R. Yager, “Soft Approaches toDistributed Information Retrieval”, International Journal ofApproximate Reasoning, vol.34, pp.105-120, 2003.

[45]D.H. Kraft , M.J. Martın-Bautista , J. Chen ,D. Sanchez, “Rulesand Fuzzy Rules in Text: Concept, Extraction and Usage”,International Journal of Approximate Reasoning, vol.34, pp. 145-161, 2003.

[46]Sadaaki Miyamoto, “Proximity Measures for Terms Based onFuzzy Neighborhoods in Document Sets”, International Journal ofApproximate Reasoning, vol.34, pp. 181-199, 2003.

[47]Jose A. Olivas , Pablo J. Garces , Francisco P. Romero, ”AnApplication of the FIS-CRM Model to the FISS Metasearcher: UsingFuzzy Synonymy and Fuzzy Generality for Representing Conceptsin Documents”, International Journal of Approximate Reasoning, 34,pp. 201-219, 2003.

[48]E. Herrera-Viedma , O. Cordon , M. LuqueA, G. Lopez, “A Model ofFuzzy Linguistic IRS Based on Multi-Granular LinguisticInformation”, International Journal of Approximate Reasoning,vol.34, pp. 221-239, 2003.

[49]J.L. Fernandez-Villacanas, M. Shackleton, “Investigation of theImportance of the Genotype– Phenotype Mapping in InformationRetrieval”, Future Generation Computer Systems ,vol.19, pp. 55–68,2003.

[50]R.L. Walker, “Assessment of the Web Using GeneticProgramming”, Proc. Genetic and Evolutionary ComputationConference, San Francisco, pp. 1750–1755, 1999.

[51]Bergstrom, P. Jaksetic, P. Nordin, “Enhancing InformationRetrieval by Automatic Acquisition of Textual Relations UsingGenetic Programming”, Proc. International Conference on IntelligentUser Interfaces, New Orleans, USA, pp. 29–32, 2000.

[52] S. Acid, L.M. de Campos, J.M. Fern_andez-Luna, J.F. Huete,“An Information Retrieval Model Based on Simple BayesianNetworks”, International Journal of Intelligent Systems , vol.18,No.2,pp. 251–265, 2003.

[53]P. Calado, A.S. Da Silva, R.C. Vieira, A.H.F. Laender, B.A. Ribeiro-Neto, “Searching Web Databases by Structuring Keyword-BasedQueries”, Proceedings of the Eleventh International Conference onInformation and Knowledge Management, ACM Press, pp. 26–33,2002.

142

[54]J.P. Callan, “Document Filtering with Inference Networks”,Research and Development in Information Retrieval, Zurich,Switzerland, 1996, pp. 262–269, August 1996.

[55]L.M. de Campos, J.M. Fernandez-Luna, J.F. “Systems using aBayesian Network-based Thesaurus”, Proceedings of the FourteenthAnnual Conference on uncertainty in Artificial Intelligence (UAI–98),Morgan Kaufmann Publishers, San Francisco, CA, pp. 53–60, 1998.

[56]S. Dumais, J. Platt, D. Heckerman, M. Sahami, “InductiveLearning Algorithms and Representations for Text Categorization”,Proceedings of the Seventh International Conference on Informationand Knowledge Management, ACM Press, pp. 148–155, 1998.

[57]D. Haines, W.B. Croft, “Relevance Feedback and InferenceNetworks”, Proceedings of the Sixteenth Annual International ACMSIGIR Conference on Research and Development in InformationRetrieval, Pittsburgh, PA, 1993, pp. 2–11, 1993.

[58]J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks ofPlausible Inference, Morgan Kaufmann Publishers, 1988.

[59]B. Ribeiro-Neto, R. Muntz, “A Belief Network Model for IR”,Proceedings of the 19th Annual International ACM SIGIR Conferenceon Research and Development in Information Retrieval, ACM Press,pp. 253–260, 1996.

[60] D. Shin, S. Nam, M. Kim, “Hypertext Construction UsingStatistical and Semantic Similarity”, Proceedings of the SecondACM International Conference on Digital Libraries, ACM Press, pp.57–63, 1997.

[61] H.R. Turtle, W.B. Croft, “Inference Networks for DocumentRetrieval”, J.L. Vidick (Ed.),SIGIR’90, 13th International Conferenceon Research and Development in Information Retrieval, Brussels,Belgium, 5–7 September1990, Proceedings, ACM Press, pp.1–24,1990.

[62]Marco Antonio Pinheiro de Cristo Pavel Pereira Calado , Maria deLourdes da Silveira , Ilmerio Silva , Richard Muntz, “BayesianBelief Networks for IR”, International Journal of ApproximateReasoning, vol.34, pp.163–179, 2003.

[63]Luis M. de Campos , Juan M. Fernandez-Luna, Juan F. Huete,“The BNR Model: Foundations and Performance of A BayesianNetwork-Based Retrieval Model”, International Journal ofApproximate Reasoning , vol.34, pp. 265–28, 2003.

[64]Vicente P.Guerrero-Bote, Cristina Lopez-Pujalte, Felix de Moya-anegon, Victor Herrero-Solana, ”Comparison of Neural Models forDocument Clustering”, International Journal of ApproximateReasoning , vol.34, pp.287-305, 2003.

[65]Daniel Crabtree, Peter Andreae and Xiaoying Gao, "Query DirectedWeb Page Clustering", International conference on Web Intelligence,pp. 202-210, December 2006.

143

[66]Jingyu Hou and Yanchun Zhang, ”Utilizing Hyperlink Transitivityto Improve Web Page Clustering”, Fourteenth Australasian DatabaseConference (ADC2003), Adelaide, Australia, 2003.

[67]Xiaofeng He, Chris H.Q. Ding, Honguan Zha, Horst D. Simon,”Automatic Topic Identification Using Web Page Clustering”, ICDM,Proceedings of the 2001 IEEE International Conference on DataMining, pp. 195 – 202, 2001.

[68]Lin Li, Zhenglu Yang, Kulwadee Somboonviwat, and MasaruKitsuregawa, “User-Assisted Similarity Estimation for SearchingRelated Web pages”, Proceedings of the Eighteenth Conference onHypertext and Hypermedia, Manchester, UK, pp. 11 – 20, 2007.

[69]Xuanhui Wang, Dou Shen, Hua-Jun Zeng, Zheng Chen, Wei-YingMa, “Web Page Clustering Enhanced by Summarization”,Proceedings of the thirteenth ACM international conference onInformation and knowledge management, Washington, D.C., USA ,pp. 242 – 243, 2004.

[70]Yitong Wang and Masaru Kitsuregawa, “ Evaluating Contents-LinkCoupled Web Page Clustering for Web Search Results”, Proceedingsof the Eleventh International Conference on Information andKnowledge Management, McLean, Virginia, USA, pp. 499 –506, 2002.

[71]John Zukos and Brijesh Venna , “An Encoding Technique basedon Word Importance for the Clustering of Web Documents”,Proceedings of the 9th international Conference on NeuralInformation Processing (ICONIP’OZ),18-22 Nov. 2002Volume: 5, pp. 2207- 2211, November 2002.

[72]Khaled M. Hammouda, Mohamed S. Kamel, “Efficient Phrase-Based Document Indexing for Web Document Clustering”, IEEETransactions on Knowledge and Data Engineering, vol. 16, No. 10,pp. 1279-1296, October 2004.

[73]Menahem Friedman and Abraham Kandel, “A Fuzzy-BasedAlgorithm for Web Document Clustering”, IEEE Annual Meeting ofthe Fuzzy Information, 2004. Processing NAFIPS '04, 27-30 June2004, vol.2, pp. 524- 527, June 2004.

[74]Yan Gao, Shiwen Gu, Liming Xia and Yaoping Fei, “WebDocument Clustering with Multi-view Information Bottleneck”,International Conference computational Intelligence for ModellingControl and Automation, and International Conference on IntelligentAgents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06) , Nov. 28 -Dec. 1, 2006, pp. 148-148, 2006.

[75]B.F.Momin, P.J.Kulkarni and A.A.Chaudhari, “Web DocumentClustering Using Document Index Graph”, International Conferenceon Advanced Computing and Communications, 2006. ADCOM 2006,20-23 Dec, Surathkal, India, pp. 32-37, 2006.

144

[76]A.Chen, N.Chen, W.Jia and S.D.Luo, “Fuzzy Concept Graph andApplication in Web Clustering”, International Conferences on Info-tech and Info-net, Beijing, vol.3, pp. 101-106, 2001.

[77]Jiang-chun Song, Jun-yi Shen, “A Web Document ClusteringAlgorithm based on Concept of Neighbor”, International Conferenceon Machine Learning and Cybernetics, 2003, 2-5 Nov. 2003 vol.1, pp. 46- 50, November 2003.

[78]Mei-Ling Shyu Shu-Ching Chen, Min Chen Stuart, H. Rubin,“Affinity-Based Similarity Measure for Web Document Clustering”,Proceedings of the 2004 IEEE International Conference onInformation Reuse and Integration, 8-10 Nov, pp. 247- 252, 2004.

[79]William-Chandra Tjhi and Lihui Chen, “Fuzzy Co-clustering ofWeb Documents”, International Conference on Cyberworlds,Singapore, 2005, 23-25 Nov, pp.545-551, 2005.

[80]Somjit Arch-int , “Web Document Clustering using Semantic LinkAnalysis”, International Conference on Intelligent Agents, WebTechnologies and Internet Commerce, 28-30 Nov,Vienna,Austria, pp.13-18,2005.

[81]Jongkol Janruang and Worapoj Kreesuradej, “A New Web SearchResult Clustering based on True Common Phrase Label Discovery”,International Conference on Computational Intelligence forModelling, Control and Automation, 2006 and InternationalConference on Intelligent Agents, Web Technologies and InternetCommerce, Nov.28 2006-Dec.1 2006, pp. 242-242 , Sydney, 2006.

[82]Licai Yang Lancang Yang, “Study of a Cluster Algorithm Based onRough Sets Theory”, Sixth International Conference on IntelligentSystems Design and Applications, 2006. ISDA '06, 16-18 Oct. 2006vol.1, pp. 492-496, 2006.

[83]William-Chandra Tjhi te al, “Flexible Fuzzy Co-clustering withFeature-cluster Weighting”, 9th International Conference on Control,Automation, Robotics and Vision, 2006. ICARCV '06.5-8 Dec. 2006,Singapore, pp.1-6 , 2006.

[84]Omar Alonso and Michael Gertz , “Clustering of Search Resultsusing Temporal Attributes”, Proceedings of the 29th annualinternational ACM SIGIR conference on Research and development ininformation retrieval Seattle, Washington, USA pp. 597 – 598,2006.

[85]Hsi-Cheng Chang and Chiun-Chieh Hsu, “Using Topic KeywordClusters for Automatic Document Clustering”, Proceedings of the2004 IEEE International Conference on Information Reuse andIntegration, 2004. IRI 2004, 8-10 Nov. 2004, pp. 247- 252, Nov.2004.

[86]W. Xu, X. Liu, and Y. Gong, “Document Clustering Based On Non-negative Matrix Factorization”, Proceedings of the 26th annual

145

international ACM SIGIR conference on Research and development inInformation, Toronto, Canada, pp. 267 – 273, 2003.

[87]Han-joon Kim and Sang-goo Lee, “An Effective DocumentClustering Method using User-Adaptable Distance Metrics”,Proceedings of the 2002 ACM symposium on Applied computingMadrid, Spain ,pp. 16 – 20, 2002.

[88]T. Korenius, J. Laurikkala, K. Järvelin, and M. Juhola, “Stemmingand Lemmatization in the Clustering of Finnish Text Documents”,Proceedings of the thirteenth ACM international conference onInformation and knowledge management, Washington, D.C., USA,pp. 625 – 633, 2004.

[89]Kai li, Li-juan cui , “Study of Clustering Algorithm based on ModelData”, International Conference on Machine Learning andCybernetics, 19-22 Aug, vol.7, Hong Kong, pp. 3961-3964 , August2007.

[90]Magne Setnes , “Supervised Fuzzy Clustering for Rule Extraction”,IEEE Transactions on Fuzzy Systems, vol.8, Issue 4, pp. 416-424,2000.

[91]Mika SATO and Yoshiharu SATO, “Fuzzy Clustering Model forFuzzy Data”, International Joint Conference of the Fourth IEEEInternational Conference on Fuzzy Systems and The SecondInternational Fuzzy Engineering Symposium, 20-24 Mar, vol.4, pp.2123-2128, Mar 1995.

[92]Zhengyu Zhu, Yunyan Tian, Jingqiu Xu, Xin Deng, Xiang Ren, "AnImproved Partitioning-Based Web Documents Clustering MethodCombining GA with ISODATA," Fourth International Conference onFuzzy Systems and Knowledge Discovery (FSKD 2007), vol.2,pp.208-213, 2007.

[93]Yan Yang, Mohammed Kamil, Fin Yang, ”A Model of DocumentClustering Using Ant Colony Algorithm and Validity Index”,Proceedings of IEEE International Joint Conference on NeuralNetworks, 31 July-4 August, vol.5, pp. 2730- 2735, 2005.

[94]Marti A.Hearst and Jan O.Pederson, “Reexamining the ClusterHypothesis: Using Scatter-Gather on Retrieval Results”,Proceedings of ACM SIGIR’96, Proceedings of the 19th annualinternational ACM SIGIR conference on Research and development ininformation retrieval , Zurich, Switzerland, pp. 76 - 84, 1996.

[95]Ying Zhao and George Karypis, “Hierarchical ClusteringAlgorithms for Document Datasets”, Proceedings of the eleventhinternational conference on Information and knowledge management,McLean, Virginia, USA, Web Clustering, pp.515-524, 2002.

[96]Oren Zamir and Oren Etzioni, “Web Document Clustering: AFeasibility Demonstration”, Proceedings of ACM SIGIR’98,Melbourne, Australia, pp. 46-54, 1998.

146

[97]Jerzy Stefanowski and Dawid Weiss, “Carrot2 and LanguageProperties in Web Search Results Clustering”, Atlantic WebIntelligence Conference, Madrid, Spain, vol. 2663, pp. 240-249,2003.

[98]Oren Zamir and Oren Etzioni, “Grouper: A Dynamic ClusteringInterface to Web Search Results”, Computer Networks, Vol. 31, No.11-16, pp. 1361-1374, 1999.

[99]Jingyu Hou and Yanchun Zhang, “A Matrix Approach forHierarchical Web Page Clustering based on Hyperlinks”,Proceedings of the Third International Conference on WebInformation Systems Engineering, pp. 207-216, 2002.

[100]R. A. Shokry, A. A. Saad, N. M. El-Makkey, M. A. Ismail, “UsingNew Soft Clustering Technique in Adaptive Web Site”, Proceedingsof the 2006 IEEE/WIC/ACM International Conference on WebIntelligence and Intelligent Agent, pp.281-286, 2006.

[101]Shi Zhong and Joydeep Ghosh, “A Comparative Study ofGenerative Models for Document Clustering”, SIAM Int. Conf. DataMining Workshop on Clustering High Dimensional Data and ItsApplications, 2003.

[102]Michael Steinbach, George Karypis and Vipin Kumar, ”AComparison of Document Clustering Techniques”, KDD Workshopon Text Mining, 2000.

[103]V. Loia, W. Pedrycz and S. Senatore , “P -FCM: A Proximity-Based Fuzzy Clustering for User-Centered Web Applications”,International Journal of Approximate Reasoning ,vol.34, pp.121–144, 2003.

[104]Vicente P.Guerrero-Bote, Cristina Lopez-Pujalte, Felix de Moya-anegon, Victor Herrero-Solana, “Comparison of Neural Models forDocument Clustering”, International Journal of ApproximateReasoning , vol.34 pp. 287–305, 2003.

[105]Jian Yu, “General C-Means Clustering Model”, IEEE Transactionson Pattern Analysis and Machine Intelligence, vol. 27, no.8, pp.1197-1211, Aug. 2005

[106]Luying Liu, Jianchu kang, Jing yu, Zhongliang wanga,“Comparative Study on Unsupervised Feature Selection Methodsfor Text Clustering”, Proceedings ofNLP-KE'O5, 2005, pp.597-601,2005.

[107]Shoushan Li and Chengqing Zong, “A New Approach to FeatureSelection for Text Categorization”, Proceedings of NLP-KE'O5, pp.626-630, 2005.

[108]Xiubo Geng, et al, “Feature Selection for Ranking”, Proceedings ofthe 30th annual international ACM SIGIR conference on Researchand development in information retrieval, Amsterdam, TheNetherlands, pp. 407 - 414, 2007.

147

[109]Anirban Dasgupta et al, “Feature Selection Methods for TextClassification”, Proceedings of the 13th ACM SIGKDD internationalconference on Knowledge discovery and data mining, San Jose,California, USA ,pp. 230 - 239, 2007.

[110]Daniele Riboni, “Feature Selection for Web Page Classification”,EURASIA-ICT 2002 Proceedings of the workshop, pp.473-477, 2002.

[111]R.K.Mishra, T.V.Prabhakar, “KhojYantra: An IntegratedMetaSearch Engine with Classification, Clustering and RankingIdeas”, International Database Engineering and ApplicationsSymposium (IDEAS'00), pp.122, 2000.

[112]Yitong WANG and Masaru Kitsuregawa, “USE Link-BasedClustering to Improve Web Search Results”, Proceedings of theSecond International Conference on Web Information SystemsEngineering (WISE'01, vol.1, pp.115, 2001.

[113]Filippo Ricca, Paolo Tonella, Christian Girardi, Emanuele Pianta,"An Empirical Study on Keyword-based Web Site Clustering", ICPC,12th IEEE International Workshop on Program Comprehension(IWPC'04), pp.204, 2004.

[114]Chi Lang Ngo, Hung Son Nguyen, “A Method of Web SearchResult Clustering based on Rough Sets”, Proceedings of the 2005IEEE/WIC/ACM International Conference on Web Intelligence(WI’05),2005, pp. 673 – 679, 2005.

[115]Fang Li Mehlitz, M. Li Feng Huanye Sheng , “Web PagesClustering and Concepts Mining: An approach towards IntelligentInformation Retrieval”, IEEE Conference on Cybernetics andIntelligent Systems, 2006,pp.1-6, 2006.

[116]Hui Zhang, Bin Pang,Ke Xie,Hui Wu, “An Efficient Algorithm forClustering Search Engine Results, Computational Intelligence andSecurity”, International Conference, CIS 2006, Guangzhou, China,November 3-6, 2006, pp. 661 - 671, 2007.

[117]Burak Ozyurt and Greg G. Brown, “Search Result Refinement viaMachine Learning from Labeled-Unlabeled Data for Meta-search”,Proceedings of the 2007 IEEE Symposium on ComputationalIntelligence and Data Mining (CIDM 2007),pp.186-193, 2007.

[118]Chun-Wei Tsai et al., “A Document Clustering Approach forSearch Engines”, IEEE International Conference on Systems, Man,and Cybernetics, Taipei, Taiwan,pp.1050-1055, 2006.

[119]Yi Zhang, Lidong Bing, Yexin Wang, Yan Zhang, "LET: TowardsMore Precise Clustering of Search Results," Fourth InternationalConference on Fuzzy Systems and Knowledge Discovery (FSKD2007) Vol.2, pp.385-389, 2007.

[120]Crabtree, D., Gao, X., and Andreae, P. “Standardized EvaluationMethod for Web Clustering Results”, Proceedings of the 2005IEEE/WIC/ACM International Conference on Web intelligence,

148

September 19-22,IEEE Computer Society, Washington, DC,pp.280-283, 2005.

[121]Mojica, J. A., Rojas, D. A., Gomez, J., and Gonzalez, F., “PageClustering Using a Distance-Based Algorithm”, Proceedings of theThird Latin American Web Congress, October 31 - November 02,IEEE Computer Society, Washington, DC, page 223, 2005.

[122]Yi-Ouyang, Y., Yun-Ling, Y., and AnDing-Zhu, A., “EHM-BasedWeb Pages Fuzzy Clustering Algorithm”, Proceedings of the 2007international Conference on Multimedia and Ubiquitous Engineering,April 26 - 28, 2007,MUE, IEEE Computer Society, Washington,DC, pp.561-566, 2007.

[123]Fresno, V. Martinez, R. Montalvo, S, “Improving Web PageClustering through Selecting Appropriate Term WeightingFunctions”, Proceedings of the 1st International Conference onDigital Information Management, Bangalore, pp. 511-518,2006.

[124]Mehlitz, M. Bauckhage, C. Albayrak, S.,”A Fast, Feature-basedCluster Algorithm for Information Retrieval”, IEEE InternationalConference on Information Reuse and Integration, Las Vegas ,335-341 , 2007.

[125]Ming Hei Tsui Lim, B. Daming Shi , “Web Search ResultRefinement by Document Clustering”, IEEE InternationalConference on Systems, Man and Cybernetics, Montreal,pp.3081-3086, 2007.

[126]M.M. Sufyan Beg and Nesar Ahmad, “Soft ComputingTechniques for Rank Aggregation on the WorldWideWeb”, WorldWide Web: Internet and Web Information Systems, vol. 6, KluwerAcademic Publishers, pp. 5–22, 2003.

[127]Si, L. and Callan, J. 2002. “Using Sampled Data and Regressionto merge Search Engine Results”, Proceedings of the 25th Annualinternational ACM SIGIR Conference on Research and Developmentin information Retrieval , Tampere, Finland, pp.19-26, 2002.

[128]Sankar K. Pal, “Web Mining in Soft Computing Framework:Relevance, State of the Art and Future Directions”, IEEETransactions on Neural Networks, vol.13, no.5, pp. 1163-1177,2002.

[129]Tie-Yan Liu et al, “LETOR: Benchmark Dataset for Research onLearning to Rank for Information Retrieval”, SIGIR 2007 Workshopon Learning to Rank for Information Retrieval (LR4IR 2007), pp.3-10,2007.

[130]Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning aboutData, Kluwer Academic Publishing, Dordrecht. 1991.

[131]G. Forman, “An Extensive Empirical Study of Feature SelectionMetrics for Text Classification”, Journal of Machine LearningResearch, vol. 3, pp. 1289– 1305, 2003.

149

[132]Chouchoulas and Q. Shen. “Rough Set-Aided Keyword Reductionfor Text Categorisation”, Applied Artificial Intelligence, vol. 15, No.9, pp. 843–873, 2001.

[133]Duntsch and G. Gediga, “Rough Set Data Analysis”. In: A. Kent &J. G.Williams (Eds.) Enc Encyclopedia of Computer Science andTechnology, Vol. 43, No. 28, pp. 281–301, 2000.

[134]H. Sever, “The Status of Research on Rough Sets for KnowledgeDiscovery in Databases”, Proceedings of the Second InternationalConference on Nonlinear Problems in Aviation and Aerospace(ICNPAA98), Vol. 2, pp. 673–680, 1998.

[135]Skowron, Z. Pawlak, J. Komorowski and L. Polkowski, “A RoughSet Perspective on Data and Knowledge”, Handbook of data miningand knowledge discovery, Oxford University Press, pp. 134–1492002.

[136]Skowron, S.K. Pal, “Special Issue: Rough Sets, PatternRecognition and Data Mining”, Pattern Recognition Letters, Vol. 24,No. 6, pp. 829–933, 2003.

[137]Chouchoulas, J. Halliwell and Q. Shen, “On the Implementationof Rough Set Attribute Reduction”, Proceedings of the 2002 UKWorkshop on Computational Intelligence, pp. 18–23, 2002.

[138]Han, J., Hu, X., Lin, T. Y. , “Feature Subset Selection Based onRelative Dependency between Attributes. Rough Sets and CurrentTrends in Computing”, 4th International Conference, RSCTC 2004,Uppsala, Sweden, June 1-5, pp. 176–185, 2004.

[139]Zhong, N., Dong, J., & Ohsuga, S, “Using Rough Sets withHeuristics for Feature Selection”, Journal of Intelligent InformationSystems, Vol. 16, No. 3, pp. 199–214, 2001.

[140]Zhang, M., & Yao, J.T., “A Rough Sets Based Approach toFeature Selection”, Proceedings of the 23rd International Conferenceof NAFIPS, Banff, Canada, June 27-30, pp. 434–439, 2004.

[141]N.M. Parthalain, Q. Shen, R. Jensen, “Distance Measure assistedRough Set Feature Selection”, Fuzzy Systems Conference, FUZZ-IEEE 2007, July 2007, pp. 1-6, 2007.

[142]http://archive.ics.uci.edu/ml/.[143]http://www.cs.waikato.ac.nz/ml/weka/.[144]Lawrence, S. and Giles, L. “Accessibility and Distribution of

Information on the Web”, Nature, vol.400, pp.107-109,1999.(http://wwwmetrics.com).

[145]http://dir.yahoo.com.[146]T. Tsukada, M. Washio and H. Matoda, “Automatic Web-Page

Classification by using Machine Learning Methods”, Proceedings ofthe First Asia-Pacific Conference on Web Intelligence(WI2001), LNAI2198, pp.303–313, 2001.

150

[147]G. Salton, A. Wong and C.S. Yang, “A Vector Space Model forAutomatic Indexing”, Communications of the ACM, Vol. 18, No. 11,pp. 613–620, 1975.

[148]G. Salton, and C. Buckley, “Term Weighting Approaches inAutomatic Text Retrieval”, Technical Report TR87-881, Departmentof Computer Science, Cornell University. 1987.

[149]G. Salton, E.A. Fox and H. Wu, “Extended Boolean InformationRetrieval”, Communications of the ACM, Vol. 26, No. 12, pp. 1022–1036, 1983.

[150]F. Sebastiani, “Machine learning in Automated TextCategorisation”, ACM Computing Surveys, Vol. 34, No. 1, pp. 1–47,2002.

[151]Daniele Riboni, “Feature Selection for Web Page Classification”,EURASIA-ICT 2002 Proceedings of the workshop, pp.473-477, 2002.

[152]http://www.tartarus.org/~martin/PorterStemm.[153]Dean,J., Henzinger,M, “Finding Related Pages in the World Wide

Web”, Proceedings of the 8th International World Wide WebConference, pp. 389-401, 1999.

[154]R.Weiss, B.Velez, M.A.Sheldon et al., “A Hierarchical NetworkSearch Engine That Exploits Content Link Hypertext Clustering”,Proceedings of the seventh ACM Conference on Hypertext, pp. 180-193, 1996.

[155]Zamir,O., Etzioni,O., “Web Document Clustering: A FeasibilityDemonstration”, Proceedings of ACM SIGIR’98, Melbourne,Australia, pp. 46-54, 1998.

[156]Kleinberg, J., “Authoritative Sources in a HyperlinkedEnvironment”, Journal of the ACM (JACM), Vol.46, Issue 5,pp.604 – 632,1999 .

[157]Wen,C.W., Liu,H., Wen,W.X., Zheng,J., “A DistributedHierarchical clustering System for Web Mining”, Proceedings of theSecond International Conference on Web Age InformationManagement (WAIM2001),XI”AN,china,9-11 july, pp. 103-113,2001.

[158]Chakrabarti, S., Dom, B., Raghavan, P., Rajagopalan, S., Gibson,D., and Kleinberg, J., “Automatic Resource Compilation byAnalyzing Hyperlink Structure and Associated Text”, Proceedings ofThe International World Wide Web Conference, pp. 65-74, 1998.

[159]Pitkow, J., Pirolli. P. “Life Death and Lawfulness on theElectronic Frontier”, Proceedings of ACMCHI’97, Atlanta, USA, pp.383-390, 1997.

[160]Piroli, P., Pitkow , J., Rao, R., “Silk from a Sow’s Ear: ExtractingUsable Structures from the Web”, Proceedings of ACM SISCH’Conference on Human factors in Computing, Vancouver, BritishColumbia, Canada, pp. 118 – 125, 1996.

151

[161]Chakrabarti,S., Dom,B., Indyk,P., “Enhanced HypertextCategorization Using Hyperlinks”, Proceedings of the ACM SIGMODInternational Conference on Management of Data(SIGMOD’98),seattle, USA ,pp. 307-318, 1998.

[162]Bharat,K., Henzinger,M., “Improved Algorithms for TopicDistillation in Hyperlinked Environment”, Proc. of the 21’stInternational Conference of Research and development in InformationRetrieval (SIGIR 98), pp. 104-111, 2002.

[163]Berkhin,P, “Survey of Clustering Data Mining Techniques”,Technical report, Accrue Software, San Jose, CA, 2002.

[164]Jain,A.K., Murty,M.N., Flynn, P.J, “Data Clustering: A Review”,ACM Computing Surveys (CSUR), vol.31, No.3, pp. 264–323, 1999.

[165]Steinbach, M., Karypis,G., Kumar,V., “A comparison ofdocument clustering techniques”, In: KDD Workshop on TextMining, 2000.

[166]Hou, J. and Zhang, Y., “Utilizing Hyperlink Transitivity toImprove Web Page Clustering”, Proc. Fourteenth AustralasianDatabase Conference (ADC2003), Adelaide, Australia. , pp. 49-57,2003.

[167]Lin Li, Zhenglu Yang, Kulwadee Somboonviwat, MasaruKitsuregawa, “User-Assisted Similarity Estimation for SearchingRelated web pages”, Proceedings of the eighteenth conferenceon Hypertext and hypermedia , Manchester, UK, pp. 11 – 20,2007.

[168]Wang, X., Shen, D., Zeng, H., Chen, Z., and Ma, W., “Web PageClustering enhanced by Summarization”, Proceedings of theThirteenth ACM international Conference on information andKnowledge Management, pp. 242-243, 2004.

[169]B.V.Swathi, A.Govardhan, “Applications of Soft ComputingTechniques to Information Retrieval: A Study”, InternationalConference on Systemics, Cybernetics and Informatics, ICSCI 2006,Jan 04-08, 2006.

[170]B.V.Swathi, A.Govardhan, “Adopting Soft Computing approach toInformation Retrieval”, International Conference on Current trendsof Information Technology, MERG-IT, 2005.

[171]B.V.Swathi, A.Govardhan, “Find-K: A New Algorithm for Findingthe K in Partitioning Clustering Algorithm”, International Journal ofComputer Science and Communication Technologies, Vol.2, No.1,pp.268-272, July 2009.

[172]B.V.Swathi, A.Govardhan, “A Modified Rough Set Reduct for WebPage Classification”, International Journal of Computer applicationsin Engineering Technology and Sciences, October 2009.

[173]M.F. Porter, “An algorithm for Suffix Stripping”, Program, vol.14,no.3, pp. 130−137, 1980.

152

[174]Christopher D. Manning, Prabhakar Raghavan and HinrichSchütze, Introduction to Information Retrieval, Cambridge UniversityPress. 2008.

[175]Gerald Kowalski, Information Retrieval Systems Theory andImplementation, Kluwer publications, Boston, 1997.

[176]M.M. Sufyan Beg and Nesar Ahmad, “Soft ComputingTechniques for Rank Aggregation on the WorldWideWeb”, WorldWide Web: Internet and Web Information Systems, vol. 6, KluwerAcademic Publishers, pp. 5–22, 2003.

[177]http://clusty.com[178]http://costkiller.net/tribune/Tribu-PDF/intranet-vivisimo.pdf[179]http://www.zdnet.com/zdnn/stories/news/0,4586,2667216,00.

html[180]Dunn, J. C., “A Fuzzy Relative of the ISODATA Process and Its

Use in Detecting Compact Well-Separated Clusters,” Journal ofCybernetics, Vol. 3, pp.32-57, 1973.

[181]Pelleg, D. and Moore, A., “X-means: Extending K-means withefficient estimation of the number of clusters”, in Proceedings ofthe 17th International Conference on Machine Learning”, MorganKaufmann, San Francisco, CA, pp.727-734, 2000.

153

List of Publications Resulting from the Thesis

[1] B.V.Swathi, A.Govardhan, “Applications of Soft ComputingTechniques to Information Retrieval: A Study”, InternationalConference on Systemics, Cybernetics and Informatics, ICSCI 2006,Jan 04-08, 2006.

[2] B.V.Swathi, A.Govardhan, “Adopting Soft Computing Approach toInformation Retrieval”, International Conference on Current trends ofInformation Technology, MERG-IT, 2005.

[3] B.V.Swathi, A.Govardhan, “Find-K: A New Algorithm for Finding theK in Partitioning Clustering Algorithm”, International Journal ofComputer Science and Communication Technologies, Vol.2, No.1,pp.268-272, July 2009.

[4] B.V.Swathi, A.Govardhan, “A Modified Rough Set Reduct for WebPage Classification”, International Journal of Computer applications inEngineering Technology and Sciences, Vol.1, No.1, pp.12-20, July-Dec, 2009.

[5] B.V.Swathi, A.Govardhan, “A New Integrated Machine LearningApproach for Web Page Categorization”. (Communicated to Journalof Machine Learning Research (JMLR), MIT Press ).