Software

We are glad to announce the new release 1.0 of the Data Mining Template Library (DMTL).

DMTL, written in C++, is an open-source, high-performance, generic data mining toolkit. It provides a collection of generic algorithms and data structures for mining increasingly complex and informative patterns types such as: Itemsets, Sequences, Trees and Graphs.

DMTL utilizes a generic data mining approach, where all aspects of mining are controlled via a set of properties. The kind of pattern to be mined, the kind of mining approach to use, and the kind of data types and formats to mine over are all specified as a list of properties. This provides tremendous flexibility to customize the toolkit for various applications.

New to release 1.0 is a persistent file manager that allows the database to scale beyond main memory limits. The sequence mining can now correctly handle both induced and embedded sequences, after a bug was corrected. Also new is support for multiset mining. Finally, a new property for tokenizer has been added, and various minor changes have been made to make the library more robust.

DMTL can be downloaded from the sourceforge site: http://sourceforge.net/projects/dmtl

The project file contains the source code, test examples, extensive documentation as well as related papers.

The DMTL Team Includes:
Mohammed J. Zaki -- PI
Mohammed Al Hasan, Vineet Chaoji, and Saeed Salem -- PhD Students
Computer Science Department
Rensselaer Polytechnic Institute

DMTL project is funded in part by Information Technology Innovation Center -- Knowledge Discovery and Dissemination program; National Science Foundation -- Information and Data Management program (IIS-0092978), and Next Generation Software program (EIA-0103708); and Department of Energy, Office of Science (DE-FG02-02ER25538).