By Olfa Nasraoui, Myra Spiliopoulou, Jaideep Srivastava, Bamshad Mobasher, Brij Masand

This publication constitutes the completely refereed post-proceedings of the eighth overseas Workshop on Mining internet information, WEBKDD 2006, held in Philadelphia, PA, united states in August 2006 along with the twelfth ACM SIGKDD foreign convention on wisdom Discovery and information Mining, KDD 2006.

The thirteen revised complete papers awarded including a close preface went via rounds of reviewing and development and have been rigorously chosen for inclusion within the booklet. the improved papers convey new applied sciences from parts like adaptive mining equipment, move mining algorithms, options for the Grid, specifically flat texts, files, photos and streams, usability, e-commerce functions, personalization, and suggestion engines.

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Additional info for Advances in Web Mining and Web Usage Analysis: 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006 Philadelphia, USA, August 20,

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A web page gets a high page rank if it has a large number of backlinks (a lot of pages pointing to it) or if it has backlinks from popular pages (pages that have very high page ranks) [11]. The page rank of a page is the sum of the weights of each of its incoming links and the PageRank of a page is equally distributed among its out links. Figure 1, reproduced from [11] gives an overview of PageRank calculation. Incorporating Usage Information into Average-Clicks Algorithm 23 Fig. 1. Simplified PageRank Calculation.

It is obvious that the Bimax algorithm finds a large number of overlapping biclusters. To avoid this we can perform a secondary filtering procedure to reduce this number to the desired overlapping degree. In Figure 4, we have applied the Bimax algorithm to the running example. , |Ib | ≥ 2). These bilcusters are summarized as follows: b1 : Ub1 = {U3 , U6 }, Ib1 = {I1 , I7 } b2 : Ub2 = {U5 , U7 , U2 }, Ib2 = {I5 , I3 } b3 : Ub3 = {U2 , U8 }, Ib3 = {I6 , I5 } b4 : Ub4 = {U8 , U4 }, Ib4 = {I4 , I2 , I6 } Nearest-Biclusters Collaborative Filtering with Constant Values 45 We have to notice that there is overlap between biclusters.

In: WWW 2001. Proceedings of the tenth international conference on World Wide Web, pp. 430–437. ACM Press, New York (2001) 14. , Inc. com 15. : Web-log mining for quantitative temporal-event prediction. IEEE Computational Intelligence Bulletin 1(1), 10–18 (2002) 16. : Web-log mining for predictive web caching. edu Abstract. A number of methods exists that measure the distance between two web pages. Average-Clicks is a new measure of distance between web pages which fits user’s intuition of distance better than the traditional measure of clicks between two pages.

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