 Algorithms for Extracting Frequent Episodes in the Process of Temporal Data MiningAlexandru Pirjan () Informatica Economica, 2010, vol. 14, issue 3, 165-178 Abstract: An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem. The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms. Parallel pattern discovery algorithms offer better performance and scalability, so they are of a great interest for the data mining research community. In the following, there will be highlighted some parallel and distributed frequent pattern mining algorithms on various platforms and it will also be presented a comparative study of their main features. The study takes into account the new possibilities that arise along with the emerging novel Compute Unified Device Architecture from the latest generation of graphics processing units. Based on their high performance, low cost and the increasing number of features offered, GPU processors are viable solutions for an optimal implementation of frequent pattern mining algorithms Keywords: Frequent Pattern Mining; Parallel Computing; Dynamic Load Balancing; Temporal Data Mining; CUDA; GPU; Fermi; Thread (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:14:y:2010:i:3:p:165-178 Access Statistics for this article Informatica Economica is currently edited by Ion Ivan More articles in Informatica Economica from Academy of Economic Studies - Bucharest, Romania Contact information at EDIRC. |
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