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Features

From: Gregory Piatetsky-Shapiro
Date: Jul 16, 2003
Subject: Winner of SIGKDD 2004 Innovations Award is ...

SIGKDD Innovations Award is the highest technical award in the field of data mining and knowledge discovery. It is given to one individual or one group of collaborators who has made significant technical innovations in the field of Data Mining and Knowledge Discovery that have been transferred to practice in significant ways, or that have significantly influenced direction of research and development in the field.

Previous SIGKDD Innovations Award winners were Rakesh Agrawal (IBM) in 2000, Jerome Friedman (Stanford) in 2002, and Heikki Mannila (Helsinki U. of Technology) in 2003.

ACM SIGKDD is pleased to announce the winner of SIGKDD 2004 Innovations Award, who is

Dr. Jiawei Han,

Professor, Computer Science, at Univ. of Illinois at Urbana-Champaign.

Dr. Han is widely and well regarded as a pioneer researcher in data mining and knowledge discovery, who has made many fundamental research contributions, including

  • Novel and efficient algorithms for frequent pattern mining, e.g., FP-tree and graph pattern mining algorithms
  • Attribute-oriented induction methods
  • Spatial data mining and clustering
  • Stream mining
  • Data warehousing
  • Innovative schemes to integrate OLAP, data warehouse and data mining.
The impact of Dr. Han's work is well illustrated by his paper on mining frequent patterns without candidate generation. Unlike earlier approaches that first generated candidates and then counted their support, this radically different approach essentially merged candidate generation and counting. This is done using a novel database structure, the FP-tree, which condenses the set of transactions into a form that is more compact than the original representation and amenable to a new depth-first pattern search method called FP-Growth. FP-tree based approaches are among the leading state of the art techniques for frequent itemset mining, and the concepts have also proven useful for other patterns (sequences, episodes) and pattern types (maximal, closed).

He has published more than 100 research papers on data mining in leading database and data mining conferences and journals, such as SIGMOD, VLDB, KDD, ICDE, EDBT, TKDE, and TOIS. His contribution can be seen in almost every area of the field.

Because of his many seminal contributions, Dr. Han is a very highly cited author, with over 3,000 citations, according to Citeseer. This clearly indicates the quality of his work, his influence in the field, and his contributions to many topics of data mining.

Jiawei not only is dedicated to pure research, but also industrial applications, benchmarking and products. He was the founder and chief architect of DBMiner, one of the first generation data mining products. He also actively led several projects on industrial applications of data mining techniques, which showcase the value and potential of the data mining technology.

ACM SIGKDD 2004 Awards Committee

  • Gregory Piatetsky-Shapiro (KDnuggets), Chair
  • Rakesh Agrawal (IBM)
  • Jerome Friedman (Stanford)
  • Robert Grossman (U. of Illinois, Chicago)
  • Heikki Mannila (Helsinki U. of Technology)
  • Daryl Pregibon (Google)
  • Foster Provost (New York University)
  • Sam Uthurusamy (GM)
KDnuggets : News : 2004 : n14 : item1 NEXT >

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