Istituto di Scienza e Tecnologie dell'Informazione     
Lucchese C., Boley M., Gartner T., Paurat D. Direct pattern sampling with respect to pattern frequency. In: KDLM'11 - LWA 2011 - Workshop on Knowledge Discovery, Data Mining and Machine Learning, in conjunction with the LWA 2011 (Magdeburg, Germany, 28-30 September 2011).
We present an exact and highly scalable sampling algorithm that can be used as an alternative to exhaustive local pattern discovery methods. It samples patterns according to their frequency of occurrence and can substantially improve efficiency and controllability of the pattern discovery processes. While previous sampling approaches mainly rely on the Markov chain Monte Carlo method, our procedure is direct, i.e. a non process-simulating sampling algorithm. The ad- vantages of this direct method are an almost optimal time complexity per pattern as well as an exactly controlled distribution of the produced pat- terns. In addition we present experimental results which demonstrate that these procedures can improve the accuracy of pattern-based models similar to frequent sets and often also lead to substantial gains in terms of scalability.
Subject Frequent pattern mining
H.2.8 Database Applications. Data mining

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