By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Investigators publish new report on Information Technology. According to news reporting originating from Turin, Italy, by VerticalNews correspondents, research stated, "Generalized itemset mining is a powerful tool to discover multiple-level correlations among the analyzed data. A taxonomy is used to aggregate data items into higher-level concepts and to discover frequent recurrences among data items at different granularity levels."
Our news editors obtained a quote from the research from Technical University, "However, since traditional high-level itemsets may also represent the knowledge covered by their lower-level frequent descendant itemsets, the expressiveness of high-level itemsets can be rather limited. To overcome this issue, this article proposes two novel itemset types, called Expressive Generalized Itemset (EGI) and Maximal Expressive Generalized Itemset (Max-EGI), in which the frequency of occurrence of a high-level itemset is evaluated only on the portion of data not yet covered by any of its frequent descendants. Specifically, EGI s represent, at a high level of abstraction, the knowledge associated with sets of infrequent itemsets, while Max-EGIs compactly represent all the infrequent descendants of a generalized itemset. Furthermore, we also propose an algorithm to discover Max-EGIs at the top of the traditionally mined itemsets."
According to the news editors, the research concluded: "Experiments, performed on both real and synthetic datasets, demonstrate the effectiveness, efficiency, and scalability of the proposed approach."
For more information on this research see: Expressive generalized itemsets. Information Sciences, 2014;278():327-343. Information Sciences can be contacted at: Elsevier Science Inc, 360 Park Ave South, New York, NY 10010-1710, USA. (Elsevier - www.elsevier.com; Information Sciences - www.elsevier.com/wps/product/cws_home/505730)
The news editors report that additional information may be obtained by contacting E. Baralis, Politecn Torino, Dipartimento Automat & Inform, I-10129 Turin, Italy. Additional authors for this research include L. Cagliero, T. Cerquitelli, V. D'Elia and P. Garza.
Keywords for this news article include: Turin, Italy, Europe, Information Technology
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