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

International Journal on Advanced Computer Theory and Engineering (IJACTE)for searching frequent patterns and a few algorithmsbased on construction of pattern lattice. Also they havepresented the results of the experiments they carried outto observe and analyse performances of the algorithmsthey have listed[5].Theint Theint Aye explains the importance of datapreprocessing to improve ease and efficiency of miningprocess. It mainly focuses on data preprocessing stagesand explained algorithms for field extraction and datacleaning algorithms that performs the process ofseparating fields from the single line of the log file andeliminates inconsistent or unnecessary items in theanalysed data respectively.[1]Robert Cooley et al. have presented a survey of theresearch in the area of Web usage mining and havebriefly discussed the pattern discovery techniques.III. STAGES IN WEB USAGE MININGBriefly, the stages are noted as follows:i. Obtain data from various sourcesii. Data preprocessingiii. Pattern Discoveryiv. Pattern Analysiscookies, path analysis, association rules, sequentialpatterns, clustering, decision trees etc.C. Pattern AnalysisFollowing statistics are a few listed ones which arethe end products of analysis such as the frequency ofvisits per document, most recent visit per document,who is visiting which documents, frequency of use ofeach hyperlink, and most recent use of each hyperlink.The common techniques used for pattern analysis arevisualization techniques, OLAP techniques, Data &Knowledge Querying, Usability Analysis.IV. TECHNIQUES TO DISCOVER FREQUENTPATTERNSA. Frequent Access Pattern Mining(FAP Mining)FAP algorithm is rooted on FP Growth Algorithmand it also constructs a FAP tree like FP tree. FAPalgorithm is divided into two steps. In the first step, itconstructs frequent access pattern tree (FAP tree) as perthe access paths obtained from the user sessions andrecords the access counts of each page. Next step iswhere the function of FAP-growth is used to mine bothlong and short access patterns on the FAP tree.FP Growth when used in mining association rulesand sequential patterns shows good functionality. Butthere is no sequence among those elements of an itemduring mining association rules, whereas access patternmining requires sequential page access. Thus the Fpgrowthhas been revised by X. Wang et al in [7]Table 1. Episode of user access path in users session filesFig.2. General Architecture of Web Usage Mining[14]A. Data PreprocessingThe data should be preprocessed to improve theefficiency and ease of the mining process. The main taskof data preprocessing is to prune noisy and irrelevantdata, and to reduce data volume for the patterndiscovery phase. Field Extraction and data cleaningalgorithms parse the web log records separating thefields and purging. CoveringB. Pattern discoveryFew techniques to discover patterns frompreprocessed data are listed like converting IP addressesto domain names, filtering, dynamic site analysis,Fig.3. Flowchart for algorithm to construct frequent acesspattern46ISSN (Print) : 2319 – 2526, Volume-2, Issue-3, 2013