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DMTL : A Generic Data Mining Template Library

FPM (Frequent Pattern Mining) is a data mining paradigm to extract informative patterns from massive datasets. Researchers have developed numerous novel algorithms to extract these patterns. Unfortunately, the focus primarily has been on a small set of popular patterns (itemsets, sequences, trees and graphs) and no framework for integrating the FPM process has been attempted. In this paper we introduce DMTL, a generic pattern mining library which fuses theoretical concepts from formal concept analysis and generic programming. It provides a framework that allows mining a large spectrum of patterns. We express each pattern in terms of its relational properties. Describing patterns based on their properties results in a pattern concept hierarchy. This hierarchical model is implemented using principles from generic programming. In this paper, we describe our design considerations and the subsequent implementation. Some of the challenges faced in terms of language features have also been highlighted. Apart from using the library in its entirety, we believe that some of its components, such as isomorphism checking, can be used independently. These components can definitely enrich the existing functionality provided in some of the popular libraries such as the Boost Graph Library.