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.
This work was supported by NSF Grant EIA-0103708 under the KD-D program, NSF CAREER Award IIS-0092978, and DOE Early Career
PI Award DE-FG02-02ER25538.