Generic Pattern Mining via Data Mining Template Library Nilanjana De Feng Gao Paolo Palmerini Nagender Parimi Jeevan Pathuri Benjarath Phoophakdee Joe Urban Mohammed J. Zaki 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 and graph mining. DMTL is extensible, scalable, and high-performance for rapid response on massive datasets. A detailed set of experiments show that DMTL is competitive with special purpose algorithms designed for a particular pattern type, especially as database sizes increase. Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY cs-04-01
Generic Pattern Mining via Data Mining Template Library
Nilanjana De
Feng Gao
Paolo Palmerini
Nagender Parimi
Jeevan Pathuri
Benjarath Phoophakdee
Joe Urban
Mohammed J. Zaki
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 and graph mining. DMTL is extensible, scalable, and high-performance for rapid response on massive datasets. A detailed set of experiments show that DMTL is competitive with special purpose algorithms designed for a particular pattern type, especially as database sizes increase.
Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY
cs-04-01