Practical methods for data mining with massive data sets
| dc.contributor.advisor | Scott, David W. | |
| dc.creator | Salch, John David | |
| dc.date.accessioned | 2009-06-04T08:11:47Z | |
| dc.date.available | 2009-06-04T08:11:47Z | |
| dc.date.issued | 1998 | |
| dc.description.abstract | The 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. | |
| dc.format.extent | 115 p. | en_US |
| dc.format.mimetype | application/pdf | |
| dc.identifier.callno | Thesis Stat. 1998 Salch | |
| dc.identifier.citation | Salch, 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>. | |
| dc.identifier.uri | https://hdl.handle.net/1911/19307 | |
| dc.language.iso | eng | |
| dc.rights | Copyright 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. | |
| dc.subject | Statistics | |
| dc.title | Practical methods for data mining with massive data sets | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Statistics | |
| thesis.degree.discipline | Engineering | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
Files
Original bundle
1 - 1 of 1