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dc.contributor.authorHassanpour Ghady, Saeed
dc.date.accessioned2007-09-27 20:18:37 (GMT)
dc.date.available2007-09-27 20:18:37 (GMT)
dc.date.issued2007-09-27T20:18:37Z
dc.date.submitted2007-09-26
dc.identifier.urihttp://hdl.handle.net/10012/3359
dc.description.abstractBi-clustering, i.e. simultaneously clustering the rows and columns of matrices based on their entries, covers a large variety of techniques in data mining. The goal of all bi-clustering techniques is finding the partitions of the rows and the columns in which sub-rows and sub-columns show a similar behavior. Currently existing algorithms for bi-clustering problems are either heuristic, or try to solve approximations of the original problems. There is no efficient algorithm for exact bi-clustering problems. The computational complexity of bi-clustering problems depends on the exact problem formulation, and particularly on the merit function used to evaluate the quality of a given bi-clustering partition. The computational complexity of most of the common bi-clustering problems is unknown. In this thesis, we present a formal definition for the homogeneous cover problem. This problem has many applications from bio-informatics to targeted marketing. We analyze its computational complexity and show that the problem is NP-hard.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.titleComputational Complexity of Bi-clustering.en
dc.typeMaster Thesisen
dc.pendingfalseen
dc.subject.programComputer Scienceen
uws-etd.degree.departmentSchool of Computer Scienceen
uws-etd.degreeMaster of Mathematicsen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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