
Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful patterns in massive, complex datasets. In this paper we propose the Data Mining Template Library, a collection of generic containers and algorithms for FPM, as well as persistency and database management classes. DMTL provides a systematic solution to a whole class of common FPM tasks like itemset, sequence, tree and graph mining. DMTL is extensible, scalable, and high-performance for rapid response on massive datasets. Our experiments show that DMTL is competitive with special purpose algorithms designed for a particular pattern type, especially as database sizes increase.

Constraint-Based Pattern Set Mining
Luc De Raedt Katholieke Universiteit Leuven Departement Computerwetenschappen Celestijnenlaan 200a - bus 2402 3001 Heverlee Luc.DeRaedt@cs.kuleuven.be
Abstract Local pattern mining algorithms generate sets of patterns, which are typically not directly useful and have to be further processed before actual application or interpretation. Rather than investigating each pattern individually at the local level, we propose to mine for global models directly. A global model is essentially a pattern set that is interpreted as a disjunction of these patterns. It becomes possible to specify constraints at the level of the pattern sets of interest. This idea leads to the development

Generic Pattern Mining via Data Mining Template Library
Nilanjana De, Feng Gao, Paolo Palmerini † Nagender Parimi, , Jeevan Pathuri, Benjarath Phoophakdee, Joe Urban, and Mohammed J. Zaki Computer Science Department, Rensselaer Polytechnic Institute, Troy NY 12180
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Abstract Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful patterns in massive, complex datasets. In this paper we propose the Data Mining Template Library, a collection of generic containers and algorithms for data mining, as well as persistency and database management classes. DMTL provides a systematic solution to a whole class of common FPM tasks like itemset, sequence, tree
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