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By Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum

ISBN-10: 3642005276

ISBN-13: 9783642005275

This ebook constitutes the completely refereed post-workshop lawsuits of the ninth foreign Workshop on Mining net info, WEBKDD 2007, and the first overseas Workshop on Social community research, SNA-KDD 2007, together held in St. Jose, CA, united states in August 2007 along side the thirteenth ACM SIGKDD overseas convention on wisdom Discovery and knowledge Mining, KDD 2007.

The eight revised complete papers offered including an in depth preface went via rounds of reviewing and development and have been rigorously chosen from 23 preliminary submisssions. the improved papers handle all present concerns in net mining and social community research, together with conventional net and semantic internet functions, the rising functions of the net as a social medium, in addition to social community modeling and analysis.

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Extra resources for Advances in Web Mining and Web Usage Analysis, 9 conf., WebKDD 2007

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As described in the following section, the Innovation Jam format has been used extensively within IBM. ca) in 2006. The brainstorming format within a limited time frame is recognized as a useful approach to dispersed and large-scale collaboration. Thus, it is useful to analyze the discussions, and explore how people reached the resultant “big deas” of the Jam. 3 Innovation Jam Background In 2001, IBM introduced the Jam concept through a social computing experiment to engage large portions of its global workforce in a web-based, moderated brainstorming exercise over three days [8].

Hence, we can treat this as a supervised learning problem and we can use the labeling to identify the most informative features. 38 W. Gryc et al. Testing the features for association with selection for funding. We investigated the correlation between our 18 features and the response variable — whether or not each “big idea” was selected as a finalist for funding. We applied a parametric t-test, and two non-parametric tests (Kolmogorov-Smirnov and Mann-Whitney, [5]) to test the hypothesis of a difference in the distributions P(feature|selected) and P(feature|not selected) for each of the 18 features.

O1 Average pairwise distance between the contributors within a big idea 4 . O2 Standard deviation of the pairwise distances between the contributors 4 . O3 Total number of pairwise distances between all the contributors involved. O4 Maximum pairwise distance between the contributors. O5 Minimum pairwise distance between the contributors. 046 Table 3. Description of 18 different features used in the analysis of Innovation Jam 36 W. Gryc et al. Looking for Great Ideas: Analyzing the Innovation Jam 37 Table 4.

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Advances in Web Mining and Web Usage Analysis, 9 conf., WebKDD 2007 by Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum

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