Download Advances in Knowledge Discovery and Data Mining: 5th by Hosagrahar Visvesvaraya Jagadish⋆ (auth.), David Cheung, PDF

By Hosagrahar Visvesvaraya Jagadish⋆ (auth.), David Cheung, Graham J. Williams, Qing Li (eds.)

ISBN-10: 3540419101

ISBN-13: 9783540419105

This ebook constitutes the refereed court cases of the fifth Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2001, held in Hong Kong, China in April 2001.
The 38 revised complete papers and 22 brief papers awarded have been conscientiously reviewed and chosen from a complete of 152 submissions. The booklet deals topical sections on net mining, textual content mining, purposes and instruments, thought hierarchies, characteristic choice, interestingness, series mining, spatial and temporal mining, organization mining, category and rule induction, clustering, and complicated themes and new methods.

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Extra resources for Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings

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In Proc. of 16th Conf. on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 2000. 7. L. Ungar and D. Foster. Clustering methods for collaborative filtering. In Workshop on Recommendation Systems at the Fifteenth National Conference on AI, 1998. 8. L. Ungar and D. Foster. A formal statistical approach to collaborative filtering. In CONALD’98, 1998. 9. T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the Tenth European Conference on Machine Learning, pages 137–142, 1998.

2 Other Models In the recommender system application, interpretability of the models used is an important characteristic to be considered in addition to the accuracy achieved and the computational requirements. We have included a decision tree based recommender system in this empirical study as an example of using an interpretable model. In this decision tree package, the splitting criteria during tree growth is a modified version of entropy and the tree pruning is done using a Bayesian model combination approach originated from data compression [18, 19].

Petsche. MIT Press, 1997. 6. D. Heckerman, D. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Dependency networks for collaborative filtering and data visualization. In Proc. of 16th Conf. on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 2000. 7. L. Ungar and D. Foster. Clustering methods for collaborative filtering. In Workshop on Recommendation Systems at the Fifteenth National Conference on AI, 1998. 8. L. Ungar and D. Foster. A formal statistical approach to collaborative filtering.

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