Number of the records: 1
Static Load Balancing of Parallel Mining of Frequent Itemsets Using Reservoir Sampling
- 1.
SYSNO ASEP 0368102 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Static Load Balancing of Parallel Mining of Frequent Itemsets Using Reservoir Sampling Author(s) Kessl, Robert (UIVT-O) Source Title Machine Learning and Data Mining in Pattern Recognition. - Berlin : Springer, 2011 / Perner P. - ISSN 0302-9743 - ISBN 978-3-642-23198-8 Pages s. 553-567 Number of pages 15 s. Action MLDM 2011. International Conference /7./ Event date 30.08.2011-03.09.2011 VEvent location New York Country US - United States Event type WRD Language eng - English Country DE - Germany Keywords frequent itemset mining ; parallel algorithms ; association rules ; approximate counting Subject RIV IN - Informatics, Computer Science R&D Projects GAP202/10/1333 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10300504 - UIVT-O (2005-2011) EID SCOPUS 80052336868 DOI 10.1007/978-3-642-23199-5_41 Annotation In this paper, we present a novel method for parallelization of an arbitrary depth-first search (DFS in short) algorithm for mining of all FIs. The method is based on the so called reservoir sampling algorithm. The reservoir sampling algorithm in combination with an arbitrary DFS mining algorithm executed on a database sample takes an uniformly but not independently distributed sample of all FIs using the reservoir sampling. The sample is then used for static load-balancing of the computational load of a DFS algorithm for mining of all FIs. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2012
Number of the records: 1