A project for exploratory data analysis, aiming to improve responses for large database queries. Through Bell Labs.

Contains information on current research projects involving data mining as well as links and resources.

Comprehensive Data Mining facility where all the elements of the Data Mining process coexist in one center of excellence. The Center is partnered with the latest vendors of Data Mining products covering the entire spectrum of the Data Mining process.

Project developing an industry neutral and tool neutral Data Mining process model. Starting from the embryonic knowledge discovery processes used in industry today and responding directly to user requirements.

Data mining or knowledge discovery in databases, is a new research area developing methods and systems for extracting interesting and useful information from large sets of data. University of Helsinki.

Group combining modern statistical methods, machine learning, and knowledge of specific application areas to develop new approaches to data mining. University of Toronto.

Information on KDD applications and systems. Also includes a glossary and success stories.

Data Mining Research Laboratory at Louisiana Tech University has research specialization in the arena of Bioinformatics, Clinical Imaging and knowledge discovery applications in distributed and heterogeneous data domains.

James Malone is a Research Fellow for AIAI at the University of Edinburgh. His research encompasses data mining, machine learning, Bioinformatics and Proteomics. This site also offers a free Association Rule data mining tool - the Armada software.

Non-biological intelligence concept based on a matrix reasoning algorithm representing an intelligent data understanding system. The NBI-algorithm allows for unsupervised hierarchical multi-dimensional clustering based on hundreds of parameters.

Professor of Computer Science at Monash University. Data mining, machine learning and user modeling research includes k-Optimal Rule Discovery (as exemplified by the Magnum Opus system), OPUS (an efficient search algorithm for exploring the space of conjunctive rules), learning complex conditional probabilities from data (as exemplified by the AODE algorithm), MultiBoosting, decision tree grafting and Feature Based Modeling (the first application of an association-rule-like approach to user modeling).

Multi-agent systems for incremental data-driven knowledge acquisition from distributed data sources.

Exploratory data analysis through machine learning and visualization. Work from AIRL, Dept. of CS, Iowa State University.

Group at Microsoft focused on learning from data and data mining. By building software that automatically learns from data, enable applications that do intelligent tasks such as handwriting recognition, and help human data analysts explore their data.

A long term Knowledge Discovery and Data Mining project which has the current short-term goals of research into (i) the integration of data mining with database systems and (ii) scalable data mining algorithms.

Focus on developing a resource-aware ubiquitous data mining system using different algorithmic and optimization techniques.

Intelligent Database Systems Research Laboratory. Research and development of data mining and data warehousing technology.

University of Illinois at Chicago (UIC). Serves as a national resource for research, knowledge transfer and standards development in data mining and data intensive computing.

IBM's QUEST team work on several data mining projects, especially techniques for extracting associations, classifications, sequential patterns and time sequences. They also provide a free software tool, DB2 Universal Database and Intelligent Miner, to academics for educational and resource purposes. The QUEST team were also responsible for the creation of the paradigm of Association Rules in 1993.

This website details project developments into methods and tools for analyzing large data sets and for searching for unexpected relationships in the data. The project combines development of combinatorial pattern matching algorithms with statistical techniques and database methods. The project has also studied the construction of efficient predictors from large masses of data.

A project on application of visual datamining in material research. The project uses parallel coordinates for multivariate visualization.

The XELOPES library is an open platform-independent and data-source-independent library for Embedded Data Mining. XELOPES is CWM-compatible, supports the relevant Data Mining standards and can be combined with all analytical software.