Topic modeling via scatter/gather clustering
| dc.contributor.advisor | Ghosh, Joydeep | en |
| dc.contributor.committeeMember | Bovik, Alan | en |
| dc.creator | Tyler, Marcus Mitchell | en |
| dc.creator.orcid | 0000-0002-7421-5165 | en |
| dc.date.accessioned | 2015-11-09T17:18:42Z | en |
| dc.date.available | 2015-11-09T17:18:42Z | en |
| dc.date.issued | 2015-05 | en |
| dc.date.submitted | May 2015 | en |
| dc.date.updated | 2015-11-09T17:18:42Z | en |
| dc.description | text | en |
| dc.description.abstract | Latent variable models such as Latent Dirichlet Allocation provide rich tools for analyzing large document corpora. They can uncover a wide range of hidden information such as topics in text, communities in social networks, and patterns in images. Scatter/Gather is a clustering technique that allows users to interactively combine and split groups. When joined with latent variable models, Scatter/Gather organizes topics into themes, enables topic browsing, and improves processing time for large numbers of topics. | en |
| dc.description.department | Electrical and Computer Engineering | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier | doi:10.15781/T2503R | en |
| dc.identifier.uri | http://hdl.handle.net/2152/32316 | en |
| dc.language.iso | en | en |
| dc.subject | Topic model | en |
| dc.subject | Scatter | en |
| dc.subject | Gather | en |
| dc.subject | Clustering | en |
| dc.subject | Browsing | en |
| dc.subject | Latent dirichlet allocation | en |
| dc.title | Topic modeling via scatter/gather clustering | en |
| dc.type | Thesis | en |
| thesis.degree.department | Electrical and Computer Engineering | en |
| thesis.degree.discipline | Electrical and Computer Engineering | en |
| thesis.degree.grantor | The University of Texas at Austin | en |
| thesis.degree.level | Masters | en |
| thesis.degree.name | Master of Science in Engineering | en |