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An efficient closed frequent itemset miner for the MOA stream mining system

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Quadrana, Massimo
Bifet Figuerol, Albert Carles
Gavaldà Mestre, RicardMés informacióMés informació
Document typeResearch report
Defense date2013
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.
CitationQuadrana, M., Bifet, A.C., Gavaldà, R. "An efficient closed frequent itemset miner for the MOA stream mining system". 2013. 
Is part ofLSI-13-9-R
URIhttp://hdl.handle.net/2117/99416
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  • LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge - Reports de recerca [67]
  • Departament de Ciències de la Computació - Reports de recerca [1.104]
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