Mining maximal quasi-bicliques: Novel algorithm and applications in the stock market and protein networks

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dc.contributor.author Sim K en_US
dc.contributor.author Li Jinyan en_US
dc.contributor.author Gopalkrishnan V en_US
dc.contributor.author Liu Gm en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T04:29:14Z
dc.date.available 2012-02-02T04:29:14Z
dc.date.issued 2009 en_US
dc.identifier 2011000548 en_US
dc.identifier.citation Sim K et al. 2009, 'Mining maximal quasi-bicliques: Novel algorithm and applications in the stock market and protein networks', John Wiley and Sons Inc, vol. 2, no. 4, pp. 255-273. en_US
dc.identifier.issn 1932-1864 en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/14532
dc.description.abstract Several real-world applications require mining of bicliques, as they represent correlated pairs of data clusters. However, the mining quality is adversely affected by missing and noisy data. Moreover, some applications only require strong interactions between data members of the pairs, but bicliques are pairs that display complete interactions. We address these two limitations by proposing maximal quasi-bicliques. Maximal quasi-bicliques tolerate erroneous and missing data, and also relax the interactions between the data members of their pairs. Besides, maximal quasi-bicliques do not suffer from skewed distribution of missing edges that prior quasi-bicliques have. We develop an algorithm MQBminer, which mines the complete set of maximal quasi-bicliques from either bipartite or non-bipartite graphs. We demonstrate the versatility and effectiveness of maximal quasi-bicliques to discover highly correlated pairs of data in two diverse real-world datasets. First, we propose to solve a novel financial stocks analysis problem using maximal quasi-bicliques to co-cluster stocks and financial ratios. Results show that the stocks in our co-clusters usually have significant correlations in their price performance. Second, we use maximal quasi-bicliques on a mining protein network problem and we show that pairs of protein groups mined by maximal quasi-bicliques are more significant than those mined by maximal bicliques. en_US
dc.language en_US
dc.publisher John Wiley and Sons Inc en_US
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon http://dx.doi.org/10.1002/sam.10051 en_US
dc.title Mining maximal quasi-bicliques: Novel algorithm and applications in the stock market and protein networks en_US
dc.parent Statistical Analysis and Data Mining en_US
dc.journal.volume 2 en_US
dc.journal.number 4 en_US
dc.publocation United States en_US
dc.identifier.startpage 255 en_US
dc.identifier.endpage 273 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 010400 en_US
dc.personcode 0000071819;112261;0000071821;0000071820 en_US
dc.percentage 000100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords graph mining; bicliques; finance; bioinformatics en_US
dc.staffid en_US


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