Evaluating web access log mining algorithms: a cognitive approach

The wide availability of web access logs makes themideal data sources for the mining of web behavior forelectronic commerce competitiveness. Unfortunately, sincesuch logs were originally meant for debugging purposes,they cannot be used directly for mining. Hence, much workhas been done to preprocess the logs into a suitable formand subsequently mine them. However, such existing techniquesmake various assumptions that are valid only forspecific situations. In addition, there is no fair way to comparethem objectively. In this paper, we design a frameworktermed Web Access Log Mining AlGorithm Evaluator(WALMAGE) to represent electronic commerce scenariosand web access log mining algorithms in a way that facilitatesthe choice of the most appropriate algorithm to use ina particular scenario. We propose a cognitive approach tomodel our framework so that a wide range of user behaviorcan be quantified in order to imbue our framework withrealism and practicality.

[1]  Hendrik Blockeel,et al.  Web mining research: a survey , 2000, SKDD.

[2]  Sourav S. Bhowmick,et al.  Research Issues in Web Data Mining , 1999, DaWaK.

[3]  Huberman,et al.  Strong regularities in world wide web surfing , 1998, Science.

[4]  Yannis Manolopoulos,et al.  Finding Generalized Path Patterns for Web Log Data Mining , 2000, ADBIS-DASFAA.

[5]  Anupam Joshi,et al.  On Mining Web Access Logs , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[6]  Mark Levene,et al.  Mining Association Rules in Hypertext Databases , 1998, KDD.

[7]  Yannis Manolopoulos,et al.  . EFFECTIVE PREDICTION OF WEB-USER ACCESSES: A DATA MINING APPROACH , 2001 .

[8]  Myra Spiliopoulou,et al.  Measuring the Accuracy of Sessionizers for Web Usage Analysis , 2001 .

[9]  Philip S. Yu,et al.  Efficient Data Mining for Path Traversal Patterns , 1998, IEEE Trans. Knowl. Data Eng..

[10]  Jian Pei,et al.  Mining Access Patterns Efficiently from Web Logs , 2000, PAKDD.

[11]  Jaideep Srivastava,et al.  Data Preparation for Mining World Wide Web Browsing Patterns , 1999, Knowledge and Information Systems.

[12]  Ron Kohavi,et al.  Mining e-commerce data: the good, the bad, and the ugly , 2001, KDD '01.

[13]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[14]  Andy Cockburn,et al.  What do web users do? An empirical analysis of web use , 2001, Int. J. Hum. Comput. Stud..

[15]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[16]  Philip S. Yu,et al.  Using a Hash-Based Method with Transaction Trimming for Mining Association Rules , 1997, IEEE Trans. Knowl. Data Eng..

[17]  Jaideep Srivastava,et al.  Grouping Web page references into transactions for mining World Wide Web browsing patterns , 1997, Proceedings 1997 IEEE Knowledge and Data Engineering Exchange Workshop.

[18]  Ron Kohavi,et al.  Mining e-commerce data: the good, the bad, and the ugly , 2001, KDD '01.

[19]  Philip S. Yu,et al.  Data mining for path traversal patterns in a web environment , 1996, Proceedings of 16th International Conference on Distributed Computing Systems.