Most Influential Paper Awards

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Each year the steering committee of the Pacific Asia Conference on Knowledge Discovery and Data Mining presents an award for The Most Influential Paper.

The candidates for the Most Influential Paper Award are those papers published at this conference tens years prior to the current conference. The award recognises a paper that has had significant influence over the past decade. Google Scholar is used to identify a candidate pool of papers. These papers are then reviewed by the awards committee to consider the quality of citations. An important criteria is that the paper should present novel and big ideas which change our way of thinking. A challenger/champion approach is used by the awards committee to identify the most influential paper. The awards committee informs the winner of the award.

Chun-Kit Chui (The University of Hong Kong), Ben Kao (The University of Hong Kong), Edward Hung (Hong Kong Polytechnic University). Mining Frequent Itemsets from Uncertain Data, PAKDD 2007.

Jure Leskovec, Ajit Singh, Jon M. Kleinberg. Patterns of Influence in a Recommendation Network. PAKDD 2006.

The Most Influential Paper Award is an award for a paper published at PAKDD tens years ago. The award recognises a paper that has had significant influence over the past decade. Google Scholar is used to identify a candidate pool of papers and these papers are then reviewed by the awards committee to consider the quality of citations. An important criteria is that the paper should present novel and big ideas which change our way of thinking. A challenger/champion approach is used by the awards committee to identify the most influential paper. The awards committee for 2014 was chaired by Professor Huan Liu, Arizona State University, USA, supported by Professor Joshua Huang of Shenzhen University, China, and Professor Thanaruk Theeramunkong, Thammasat University, Thailand.

The 2014 award for Most Influential Paper from PAKDD 2004 goes to Shantanu Godbole now of IBM India and Sunita Sarawagi of the Indian Institute of Technology, for their paper title Discriminative Methods for Multi-labeled Classification. The paper proposes to exploit the co-occurrence relationships of classes to label sets of documents, developing a new support vector machine ensemble to tackle the problem of multi-labelled text classification.

Enhancing Effectiveness of Outlier Detections for Low Density Patterns, by Jian TangZhixiang ChenAda Wai-Chee Fu, and David Wai-Lok Cheung. In Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, (PAKDD 2002), Lecture Notes In Computer Science, Vol 2035, Pages 247-259.

Evaluation of Interestingness Measures for Ranking Discovered Knowledge by Robert J. Hilderman and Howard J. Hamilton. Lecture Notes In Computer Science, Vol 2035. Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2001. Pages 247 – 259. This paper introduced a new framework for considering how we measure the interestingness of discoveries, and has been adopted widely by other researchers.

Feature Selection for Clustering by Manoranjan Dash and Huan Liu. Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000), Lecture Notes In Computer Science, Vol 1805, Springer, 2000. Pages 110 – 121

Mining Access Patterns Efficiently from Web Logs by Jian PeiJiawei HanBehzad Mortazavi-AslHua Zhu. Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 2000), Lecture Notes In Computer Science, Vol 1805, Springer, 2000

  • An Analysis of Quantitative Measures Associated with Rules by Yiyu Yao and Ning Zhong. Proceedings of the 3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 1999), Lecture Notes In Computer Science, Vol 1574, Springer, 1999

Clustering Large Data Sets with Mixed Numeric and Categorical Values by Zhexue Huang. Proceedings of the 1st Pacific-Asia Conference on Knowledge Discovery and Data Mining, (PAKDD 1997), Singapore, World Scientific, 1997. Pages 21-35