Advances in Web Mining and Web Usage Analysis: 8th by Olfa Nasraoui, Myra Spiliopoulou, Jaideep Srivastava,

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

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

The thirteen revised complete papers awarded including an in depth preface went via rounds of reviewing and development and have been conscientiously chosen for inclusion within the e-book. the improved papers express new applied sciences from parts like adaptive mining equipment, move mining algorithms, ideas for the Grid, specially flat texts, files, photos and streams, usability, e-commerce functions, personalization, and suggestion engines.

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Extra resources 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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Average-Clicks uses Link Analysis to measure the distance between two pages on the WWW. One inherent problem with all these methods is that all of them are heavily dependent on the link structure graph and hence are static. The dynamic nature of user behavior is not taken into consideration when assigning weights to nodes. In the Intranet Domain, useful information is available in the form of web logs which record the user sessions. User Sessions track the sequence of web pages visited by the user in addition to a lot of other information like the time spent on each page etc.

Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence, IEEE, Los Alamitos (1997) 6. : Web mining for web personalization. ACM Trans. Inter. Tech. 3(1), 1–27 (2003) 7. : Newsjunkie: Providing personalized newsfeeds via analysis of information novelty. In: WWW 2004. Proceedings of the 13th international conference on World Wide Web, pp. 482–490. ACM Press, New York (2004) 8. : Outperforming LRU with an adaptive replacement cache algorithm. Computer 37(4), 58–65 (2004) 9.

Recently, biclustering (also known as co-clustering, two-sided clustering, two-way clustering) has been exploited by many researchers in diverse scientific fields, towards the discovery of useful knowledge [2,4,5,14,19]. One of these fields is bioinformatics, and more specifically, microarray data analysis. The results of each microarray experiment are represented as a data matrix, with different samples as rows and different genes as columns. Among the proposed biclustering algorithms we highlight the following: (i) Cheng and Churchs algorithm [2] which is based on a mean squared residue score, (ii) the Iterative Signature Algorithm (ISA) which searches for submatrices representing fix points [12], (iii) the Order-Preserving Submatrix Algorithm (OPSM), which tries to identify large submatrices for which the induced linear order of the columns is identical for all rows [1],(iv) the Samba Algorithm, which is a graph theoretic approach in combination with a statistical model [27,26], and (v) the Bimax algorithm, an exact biclustering algorithm based on a divide-and-conquer strategy, that is capable of finding all maximal bicliques in a corresponding graph-based matrix representation [20].

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