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 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.
Keywords
- Association Rule
- Frequent Pattern
- Mining Algorithm
- Pattern Mining
- Formal Concept Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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.
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Zaki, M.J. et al. (2005). Towards Generic Pattern Mining. In: Ganter, B., Godin, R. (eds) Formal Concept Analysis. ICFCA 2005. Lecture Notes in Computer Science(), vol 3403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32262-7_1
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DOI: https://doi.org/10.1007/978-3-540-32262-7_1
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