Maximum total attribute relative of soft set theory for efficeint catagorical data clustering
| dc.contributor.author | Rabiei Mamat | |
| dc.date.accessioned | 2016-01-02T07:47:56Z | |
| dc.date.available | 2016-01-02T07:47:56Z | |
| dc.date.issued | 2014-03 | |
| dc.description.abstract | Clustering a set of categorical data into a homogenous class is a fundamental operation in data mining. A number of clustering algorithms have been proposed and have made an important contribution to the issues of clustering especially related to the categorical data. Unfortunately, most of the clustering techniques are not designed to address the issues of uncertainties inherent in the categorical data. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/3772 | |
| dc.language.iso | en | en_US |
| dc.publisher | Terengganu: Universiti Malaysia Terengganu | en_US |
| dc.subject | QA 76.9 .D343 R3 2015 | en_US |
| dc.subject | Rabiei Mamat | en_US |
| dc.subject | Thesis University Tun Hussein Onn Malaysia | en_US |
| dc.subject | Data mining | en_US |
| dc.title | Maximum total attribute relative of soft set theory for efficeint catagorical data clustering | en_US |
| dc.type | Thesis | en_US |
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