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Advances in Knowledge Discovery and Data Mining: 10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9-12, 2006, Proceedings: Lecture Notes in Computer Science, cartea 3918

Editat de Wee Keong Ng, Masaru Kitsuregawa, Jianzhong Li
en Limba Engleză Paperback – 31 mar 2006
The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the area of data mining and knowledge discovery. This year marks the tenth anniversary of the successful annual series of PAKDD conferences held in the Asia Pacific region. It was with pleasure that we hosted PAKDD 2006 in Singapore again, since the inaugural PAKDD conference was held in Singapore in 1997. PAKDD 2006 continues its tradition of providing an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all aspects of KDD data mining, including data cleaning, data warehousing, data mining techniques, knowledge visualization, and data mining applications. This year, we received 501 paper submissions from 38 countries and regions in Asia, Australasia, North America and Europe, of which we accepted 67 (13.4%) papers as regular papers and 33 (6.6%) papers as short papers. The distribution of the accepted papers was as follows: USA (17%), China (16%), Taiwan (10%), Australia (10%), Japan (7%), Korea (7%), Germany (6%), Canada (5%), Hong Kong (3%), Singapore (3%), New Zealand (3%), France (3%), UK (2%), and the rest from various countries in the Asia Pacific region.
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Specificații

ISBN-13: 9783540332060
ISBN-10: 3540332065
Pagini: 912
Ilustrații: XXIV, 879 p.
Dimensiuni: 155 x 235 x 48 mm
Greutate: 1.25 kg
Ediția:2006
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Keynote Speech.- Invited Speech.- Classification.- Ensemble Learning.- Ensemble Learning.- Support Vector Machines.- Text and Document Mining.- Web Mining.- Graph and Network Mining.- Association Rule Mining.- Bio-data Mining.- Outlier and Intrusion Detection.- Privacy.- Relational Database.- Multimedia Mining.- Stream Data Mining.- Temporal Data Mining.- Temporal Data Mining.- Innovative Applications.