Storage Reduction Through Minimal Spanning Trees

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dc.contributor.authorKang, Andy N. C.en
dc.contributor.authorLee, R. C. T.en
dc.contributor.authorChang, C. L.en
dc.contributor.authorChang, S. K.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2013-06-19T14:36:05Zen
dc.date.available2013-06-19T14:36:05Zen
dc.date.issued1975en
dc.description.abstractIn this paper, we shall show that a minimal spanning tree for a set of data can be used to reduce the amount of memory space required to store the data. Intuitively, the more points we have, the more likely our method will be better than the straightforward method where the data is stored in the form of a matrix. In Section 3, we shall show that once the number of samples exceeds a certain threshold, it is guaranteed that our method is better. Experiments were conducted on a set of randomly generated artificial data and a set of patient data. In the arttficial data experiment, we saved 23% for the worst case and 45% for the best case. In the patient data experiment, we saved 73% of the memory space.en
dc.format.mimetypeapplication/pdfen
dc.identifierhttp://eprints.cs.vt.edu/archive/00000788/en
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00000788/01/CS75003-R.pdfen
dc.identifier.trnumberCS75003-Ren
dc.identifier.urihttp://hdl.handle.net/10919/20263en
dc.language.isoenen
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen
dc.relation.ispartofHistorical Collection(Till Dec 2001)en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleStorage Reduction Through Minimal Spanning Treesen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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