Comparative Study of Techniques to Discover Frequent ... - IRD India

International Journal on Advanced Computer Theory and Engineering (IJACTE)Fig.11. Scalability with threshold[8]Table 4. gives a comprehensive comparisonbetween the three algorithms, GSP, FAP-Growth andDFS algorithm. Fig.11 shows the scalability in GSP andFP-growth algorithms.FP-growth works in a divide-and-conquer way. Thefirst scan of the database derives a list of frequent itemsin which items are ordered by frequency descendingorder. According to the list, the database is representedas frequent-pattern tree, or FP-tree, which shows theassociation between items. The FP-tree starts withfrequent length-1 pattern (suffix pattern), constructingits conditional pattern base (a “subdatabase”, consistingthe prefix paths in the FP-tree containing the suffixpattern), then forming conditional FP-tree, andperforming mining recursively on this tree. It uses theleast frequent items as a suffix, offering goodselectivity. Performance studies demonstrate that themethod substantially reduces search time.DFS is algorithms take a more incremental approach asit generates possible frequent sequences and uses adivide-and-conquer approach. This algorithm mainlymakes an attempt to lessen the search space.III. ACKNOWLEDGMENTI would like to thank Dr. J.W. Bakal Sir and MadhuMadam for facilitating all the necessary inputs, studymaterial and resources and guiding me with their richexperience. I would especially like to thank my parents,in-laws and my husband for their unconditional support.IV. REFERENCES[1] Theint Aye, “Web cleaning for mining of webusage patterns”, International Conference onComputer research and Development(ICCRD),pages 490-494, Vol. 2, May 2011[2] K. R. Suneetha, Dr. K. R. Krishnamoorthy,“Identifying User Behavior by Analyzing WebServer Access Log”, International Journal ofComputer Science and NetworkSecurity(IJCSNS),pages 327-331, VOL.9 No.4,April 2009[3] Guangyuan Li Qin Xiao Qinbin Hu ChanganYuan, “An Efficient Algorithm for MiningFrequent Sequences in Dynamic Environment”,in Granular Computing, 2009, GRC '09. IEEEInternational Conference, pages: 329 – 333, Aug.2009[4] Jiawei Han · Hong Cheng · Dong Xin · XifengYan , “Frequent pattern mining: current statusand future directions” ,In Proceedings ofInternational Conference on Data MiningKnowledge Discovery Journal(DATAMINE),pages 55-86, Vol. 15 No.1, March 2007[5] Murat Ali Bayir, Ismail H. Toroslu, AhmetCosar,” Performance Comparison of PatternDiscovery Methods on Web Log Data”,Computer Systems and Applications, IEEEInternational Conference, pages 445 – 451, April2006[6] Osmar R. Zaıane, Mohammad ElHajj, “PatternLattice Traversal by Selective Jumps”, in Proc.2005 Int'l Conf. on Knowledge Discovery andData Mining (ACM SIGKDD), pp 729-735,Chicago, August, 2005[7] Xidong Wang, Yiming Ouyang, Xuegang Hu,Yan Zhang, “Discovery of User Frequent AccessPatterns on Web Usage Mining”, ComputerSupported Cooperative Work in DesignProceedings 8 th IEEE International Conference,pages 765 – 769, Vol 1, November 2004[8] Jiawei Han, Jian Pei, Yiwen Yin ,Runying Mao,“Mining Frequent Patterns without CandidateGeneration: A Frequent-Pattern Tree Approach”,In Proceeding of International Conference onData Mining and Knowledge Discovery, 8, pp.53–87, 2004[9] Jian Pei, Jiawei Han, Behzad Mortazavi-Asl,Jianyong Wang, Helen Pinto, Qiming Chen,Umeshwar Dayal, Mei-Chun Hsu, “MiningSequential Patterns by Pattern-Growth: ThePrefixSpan Approach”, IEEE Transactions onKnowledge and Data Engineering, Vol. 16, No.11, November 2004[10] Jaideep Srivastava, Robert Cooleyz , MukundDeshpande, Pang-Ning Tan, “Web UsageMining: Discovery and Applications of UsagePatterns from Web Data”, In proceedings of the9th IEEE International conference on Tools with50ISSN (Print) : 2319 – 2526, Volume-2, Issue-3, 2013