Data Mining Research at the University of Vermont

[Faculty] [Publications] [Funding] [Students]

Introduction

Data mining is a broad area that integrates methods from several fields including machine learning, statistics, pattern recognition, and database systems, for the analysis of large volumes of data. The faculty at the University of Vermont have internationally recognized researchers in this area whose work is widely published in international journals and conferences. We aim to build from our acknowledged research and apply such research to large, noisy real-world problems. While also fostering continuing academic research in data mining methods and tools development, it is integral to our goals to advocate collaboration across the academic-industrial divide, and promote interdisciplinary collaborations between faculty members in Computer Science, Statistics, Engineering, Biology, and Medicine.

In recent years the power of machine learning and statistics techniques to discover interesting patterns in raw data has manifested itself in the widespread application of decision trees, rule induction, Bayesian networks, association analysis, and sequential patterns. As these techniques have matured in sophistication and power, industry has interested itself in them and become directly involved in their promotion and use, particularly in various conferences on Data Mining. The University of Vermont has a strong contingent of researchers in this area.

[Faculty] [Publications] [Funding] [Students]

Leading Forums for Data Mining Research

IEEE Transactions on Knowledge and Data Engineering (TKDE, by the IEEE Computer Society)
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI, by the IEEE Computer Society)
Data Mining and Knowledge Discovery: An International Journal (DMKD, by Springer)
Knowledge and Information Systems: An International Journal (KAIS, by Springer)
ACM Transactions on Knowledge Discovery from Data (TKDD, since 2007)
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
IEEE International Conference on Data Mining (ICDM)

Highlights of Existing Activities

Our faculty have been publishing in all of the above leading forums, and have also been publishing in other related, leading journals and conferences, such as IEEE Transactions on Information Theory, ACM Transactions on Information Systems (TOIS), Information Systems, IEEE Intelligent Systems, IJCAI, AAAI, ICML, COLT, and WWW.
Xindong Wu is the Editor-in-Chief of TKDE (IEEE Transactions on Knowledge and Data Engineering), and the Steering Committee Chair for ICDM (IEEE International Conference on Data Mining).
Jason Moore was recently appointed as the founding Co-Editor-in-Chief of BioData Mining.
Xindong Wu was appointed as one of the 2 Program Committee Co-Chairs (with Rich Caruana at Cornell University) for KDD-07: the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, to be held in San Jose, CA.

[Faculty] [Publications] [Funding] [Students]

Areas of Excellence

Data mining from multiple data sources (Xindong Wu)
Image analysis (Richard Foote, Gagan Mirchandani, and Robert Snapp)
Noise detection and cleansing in large, distributed data environments (Jeff Bond, Xindong Wu, and Xingquan Zhu)
Ontology-based information extraction and knowledge discovery (Serguei Krivov and Xindong Wu)
Pattern discovery in data streams (Byung Lee, Sean Wang, Xindong Wu, and Xingquan Zhu)
Pattern matching and mining (Abdullah Arslan, Robert Snapp, Xindong Wu, and Xingquan Zhu)
Emerging data mining applications in bioinformatics, engineering, and medicine (Abdullah Arslan, Jeff Bond, Yves Dubief, Marc Greenblatt, Larry Haugh, Yuichi Motai, Jason Moore, Adel Sadek, Jim Vigoreaux, and Xindong Wu)

Data Mining Research Links (including Journals and Conferences)

[Faculty] [Publications] [Funding] [Students]

This page has been accessed

times since November 23, 2005.
Enquiries and suggestions to: Xindong Wu (xwu@cs.uvm.edu).