By Hosagrahar Visvesvaraya Jagadish⋆ (auth.), David Cheung, Graham J. Williams, Qing Li (eds.)
This publication constitutes the refereed court cases of the fifth Pacific-Asia convention on wisdom Discovery and knowledge 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 e-book deals topical sections on internet mining, textual content mining, purposes and instruments, thought hierarchies, characteristic choice, interestingness, series mining, spatial and temporal mining, organization mining, type and rule induction, clustering, and complex subject matters and new methods.
Read Online or Download Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings PDF
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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
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. 10. C. Blake, E. J. Merz. UCI repository of machine learning databases.
Taxonomy) to predict items a new user might like. Applications of these methods include recommending items for purchase and personalizing the browsing experience on a web-site. Collaborative filtering methods have focused on using just the history of user preferences to make the recommendations. These methods have been categorized as memory-based if they operate over the entire data to make predictions and as model-based if they use the data to build a model which is then used for predictions.
A hierarchical approach to wrapper induction. In Proceedings of the 3rd International Conference on Autonomous Agents (Agents’99), Seattle, WA. 11. Muslea, I. 1999. Extraction patterns for information extraction tasks: a survey. In Proceedings of AAAI’99: Workshop on Machine Learning for Information Extraction Empirical Study of Recommender Systems Using Linear Classifiers Vijay S. Iyengar and Tong Zhang IBM Research Division, T. J. O. A. Abstract. , taxonomy) to predict items a new user might like.
Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings by Hosagrahar Visvesvaraya Jagadish⋆ (auth.), David Cheung, Graham J. Williams, Qing Li (eds.)