Practical methods for data mining with massive data sets

dc.contributor.advisorScott, David W.en_US
dc.creatorSalch, John Daviden_US
dc.date.accessioned2009-06-04T08:11:47Zen_US
dc.date.available2009-06-04T08:11:47Zen_US
dc.date.issued1998en_US
dc.description.abstractThe increasing size of data sets has necessitated advancement in exploratory techniques. Methods that are practical for moderate to small data sets become infeasible when applied to massive data sets. Advanced techniques such as binned kernel density estimation, tours, and mode-based projection pursuit will be explored. Mean-centered binning will be introduced as an improved method for binned density estimation. The density grand tour will be demonstrated as a means of exploring massive high-dimensional data sets. Projection pursuit by clustering components will be described as a means to find interesting lower-dimensional subspaces of data sets.en_US
dc.format.extent115 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoThesis Stat. 1998 Salchen_US
dc.identifier.citationSalch, John David. "Practical methods for data mining with massive data sets." (1998) Diss., Rice University. <a href="https://hdl.handle.net/1911/19307">https://hdl.handle.net/1911/19307</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/19307en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectStatisticsen_US
dc.titlePractical methods for data mining with massive data setsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentStatisticsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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