Tuple-Level Analysis for Identification of Interesting Patterns
| dc.contributor.author | Bing LIU | en_US |
| dc.contributor.author | Wynne HSU | en_US |
| dc.contributor.author | Hing-Yan LEE | en_US |
| dc.contributor.author | Lai-Fun MUN | en_US |
| dc.date.accessioned | 2004-10-21T14:28:52Z | en_US |
| dc.date.accessioned | 2017-01-23T07:00:21Z | |
| dc.date.available | 2004-10-21T14:28:52Z | en_US |
| dc.date.available | 2017-01-23T07:00:21Z | |
| dc.date.issued | 1996-05-01T00:00:00Z | en_US |
| dc.description.abstract | One of the important issues in data mining is the "interestingness" problem. Past research and applications have shown that in many situations a huge number of patterns can be discovered from a database. Most of these patterns are actually useless or uninteresting to the user. But because of the huge number of patterns, it is difficult for the user to identify those interesting to him/her. In this project, we propose a new technique to help the user identify interesting patterns. The user is first asked to provide his/her expected patterns according to his/her past knowledge and/or intuitive feelings. Given these expectations, the system uses a tuple-level fuzzy matching technique to analyze and rank the discovered patterns according to a number of interestingness measures. With this technique, the user can quickly focus on a subset of the discovered patterns with the most application values. | en_US |
| dc.format.extent | 61695 bytes | en_US |
| dc.format.extent | 472350 bytes | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.format.mimetype | application/postscript | en_US |
| dc.identifier.uri | https://dl.comp.nus.edu.sg/xmlui/handle/1900.100/1343 | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | TRA5/96 | en_US |
| dc.title | Tuple-Level Analysis for Identification of Interesting Patterns | en_US |
| dc.type | Technical Report | en_US |
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