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Recent Findings from Oklahoma State University Provides New Insights into Data Mining (A branch-and-bound approach for maximum quasi-cliques)

July 15, 2014

By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Investigators publish new report on Data Mining. According to news reporting from Stillwater, Oklahoma, by VerticalNews journalists, research stated, "Detecting quasi-cliques in graphs is a useful tool for detecting dense clusters in graph-based data mining. Particularly in large-scale data sets that are error-prone, cliques are overly restrictive and impractical."

The news correspondents obtained a quote from the research from Oklahoma State University, "Quasi-clique detection has been accomplished using heuristic approaches in various applications of graph-based data mining in protein interaction networks, gene co-expression networks, and telecommunication networks. Quasi-cliques are not hereditary, in the sense that every subset of a quasi-clique need not be a quasi-clique. This lack of heredity introduces interesting challenges in the development of exact algorithms to detect maximum cardinality quasi-cliques. The only exact approaches for this problem are limited to two mixed integer programming formulations that were recently proposed in the literature."

According to the news reporters, the research concluded: "The main contribution of this article is a new combinatorial branch-and-bound algorithm for the maximum quasi-clique problem."

For more information on this research see: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research, 2014;216(1):145-161. Annals of Operations Research can be contacted at: Springer, Van Godewijckstraat 30, 3311 Gz Dordrecht, Netherlands. (Springer -; Annals of Operations Research -

Our news journalists report that additional information may be obtained by contacting F.M. Pajouh, Oklahoma State University, Sch Ind Engn & Management, Stillwater, OK 74078, United States. Additional authors for this research include Z.Q. Miao and B. Balasundaram.

Keywords for this news article include: Oklahoma, Stillwater, United States, Information Technology, North and Central America, Information and Data Mining

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Source: Information Technology Newsweekly

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