Quine-McCluskey: a novel concept for mining the frequency patterns from web data

Quine-McCluskey: a novel concept for mining the frequency patterns from web data

Автор: Bina Bhandari, R. H. Goudar, Kaushal Kumar

Журнал: International Journal of Education and Management Engineering @ijeme

Статья в выпуске: 1 vol.8, 2018 года.

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With the advancement in the web technology it is considered as one of the vast repository of information. However this information is in the hidden form. Various data mining techniques need to be applied for extracting the meaningful information from the web. In this paper the various techniques are discussed that have been used by many researchers for extracting the information and also shown the disadvantages with the existing approaches. The paper put forward a novel concept of mining the association rule from the web data by using Quine-McCluskey algorithm. This algorithm is an optimization technique over the existing algorithm like Apriori, reverse Apriori, k-map. This paper exhibits the working of the Quine- McCluskey algorithm that can extract the frequently accessed web pages with minimum number of candidate sets generation. However the limitation of Quine-McCluskey algorithm is that it cannot find the infrequent patterns.

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Quine-Mccluskey Algorithm, K-Map, Apriori Algorithm, Users Access Pattern

Короткий адрес: https://sciup.org/15015753

IDR: 15015753   |   DOI: 10.5815/ijeme.2018.01.05

Список литературы Quine-McCluskey: a novel concept for mining the frequency patterns from web data

  • R .MohanaRanga Rao, “An Innovative Procedure To Minimize Boolean Function”, (IJAEST) International Journal Of Advanced Engineering Sciences And Technologies, Vol. No. 3, Issue No. 1, 012 – 014.
  • Hatim A. Aboalsamh, “A Novel Boolean Algebraic Framework for Association and Pattern Mining”, WSEAS TRANSACTIONS on COMPUTERS, ISSN: 1109-2750, Vol. No. 7, Issue 8, pp. 1352-1361, August 2008.
  • M. Eirinaki and M. Vazirgiannis, “Web Mining For Web Personalization”, ACM Transaction Inter. Tech., Vol. 3, No. 1, pp. 1-27, 2003.
  • J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu, “Mining Access Patterns Efficiently From Web Logs”, in PADKK ’00: Proceedings of the 4th Pacific Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications. London, UK: Springer-Verlag, pp. 396-407, 2000.
  • B. Saglam, F. S. Salman, S. Sayin, “A Mixed Integer Programming Approach To The Clustering Problem With An Application In Customer Segmentation”, European Journal of Operational Research, 173, pp. 866–879, 2006.
  • Enrique Lazcorreta, Federico Botella and Antonio Fernandez-Caballero, “Towards Personalized Recommendation By Two-Step Modified Apriori Data Mining Algorithm”,Elsvier Expert Systems with Applications 35, pp. 1422–1429, 2008.
  • C. Yun and M. Chen, “Mining Web Transaction Patterns In An Electronic Commerce Environment”, In The fourth Pacific-Asia Conference On Knowledge Discovery And Data Mining, pp. 216–219, 2000.
  • M. Karnaugh, “The Map Method For Synthesis Of Combinational Logic Circuits”, Transaction AIEE, pp. 593-599, 1953.
  • E. L. McCluskey, “Minimization Of Boolean Functions”, Bell System Technical Journal, 35, pp. 149-175, 1959.
  • G. De Micheli, “Synthesis and Optimization of Digital Circuits”, McGraw-Hill Science Engineering, 1994.
  • Murat Ali Bayir, Ismail HakkiToroslu, Murat Demirbas and AhmetCosar, “Discovering Better Navigation Sequences For The Session Construction Problem”, Elsevier, Data & Knowledge Engineering 73, pp. 58–72, 2012.
  • RanieriBaraglia, FabrizioSilvestri, Dynamic Personalization Of Web Sites Without User Intervention, Communications of the ACM 50, pp. 63–67, Feb 2007.
  • Haibin Liu, VladoKeselj, “Combined Mining Of Web Server Logs And Web Contents For Classifying User Navigation Patterns And Predicting Users' Future Requests”, Data and Knowledge Engineering Vol. 61, Issue 2, pp. 304–330, 2007.
  • Nizar R. Mabroukeh, Christie I. Ezeife, “Using Domain Ontology For Semantic Web Usage Mining And Next Page Prediction”, Proceeding of the 18th ACM Conferenceon Information and Knowledge Management, CIKM '09, ACM, New York, pp. 1677–1680, 2009.
  • Hiroshi Ishikawa, Manabu Ohta, Shohei Yokoyama, Junya Nakayama and Kaoru Katayama, “On The Effectiveness Of Web Usage Mining For Page Recommendation And Restructuring”, Lecture Notes in Computer Science, Springer Berlin, Heidelberg, Volume 2593, pp. 253–267, 2003.
  • Federico Michele Facca, Pier Luca Lanzi, “Mining Interesting Knowledge From Weblogs: A Survey”, Data & Knowledge Engineering, Vol. 53, Issue 3, pp. 225–241, 2005.
  • KR Suneetha, DR Krishnamoorthi, Identifying User Behavior By Analyzing Web Server Access Log File”, .IJCSNSInternationalJournalofComputerScienceand Network Security Vol. 9, April 4, 2009.
  • DoruTanasa, Brigitte Trousse, “Advanced Data Preprocessing For Intersites Web Usage Mining”, IEEEIntelligentSystems Vol. 19, pp. 59–65, (March–April (2)), 2004.
  • R. C. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules Between Sets Of Items In Large Databases”, In Proceeding of ACM SIGMOD International Conference on Management of Data, pp. 207–216, 1996.
  • R. C. Agrawal and R. Srikant, “Fast Algorithms For Mining Association Rules”, InProceeding of 20thInternational Conference In Large Databases, pp. 487–499, 1994.
  • D. N. Goswami, AnshuChaturvedi, and C. S. Raghuvanshi, “An Algorithm for Frequent Pattern Mining Based On Apriori”, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, Issue 04, pp. 942-947, 2010.
  • B. Kotiyal, A. Kumar, B. Pant, R.H. Goudar, “User Behavior Analysis in Web Log through Comparative Study of Eclat and Apriori”, Proceedings of7'h International Conference on Intelligent Systems and Control, pp.421-426, 2013.
  • Qiankun Zhao, Sourav S. Bhowmick, “Association Rule Mining: A Survey”, Technical Report, CAIS, Nanyang Technological University, Singapore, No. 2003116, 2003.
  • B. Kotiyal, A. Kumar, B. Pant, R.H. Goudar, “Big Data: Mining of Log File through Hadoop”, IEEE International Conference on Human Computer Interactions (ICHCI'13), at Saveetha University, Chennai, India, 23,24 August 2013.
  • B. Kotiyal, A. Kumar, B. Pant, R.H. Goudar, “Classification Technique for Improving User Access on Web Log Data”, Proceedings of International Conference on Advanced Computing, Networking, and Informatics, India, June 2013.
  • B. Kotiyal, A. Kumar, B. Pant, R.H. Goudar, “A Novel Concept For Mining Negative And Positive Rule Through Association Based K-Map”, Proceeding of International Conference on Mathematical Techniques in Engineering Applications,Oct 24-25, GEU, Dehradun, India (ICMTEA2013).
  • BamshadMobasher, Honghua Dai, Tao Luo, Miki Nakagawa, “Effective Personalization Based on Association RuleDiscovery from Web Usage Data”, WIDM01 , 3rd ACM Workshop on Web Information and Data Management,Atlanta, Georgia, USA, November 9, 2001.
  • C. Xu,C.Du, G.F.ZhaoandS.Yu, “A NovelModelforUserClicksIdentificationBasedonHiddenSemi-Markov”,Elsevier,Journal ofNetworkandComputerApplications, Vol. 36, pp. 791–798, 2013.
  • B.Santhosh Kumar, K.V.Rukmani, “Implementation of Web Usage Mining Using APRIORI and FP Growth Algorithms”, Int. J. of Advanced Networking and Applications, Vol.01, Issue6, pp. 400-404, 2010.
  • Reference for ECE320, “Quine-McCluskey's Method”.
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