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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Maddouri, M. Kaabi, F. |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Math & Comput. Sci., National Inst. of Appl. Sci. & Technol. of Tunis (Maddouri, M.; Kaabi, F.) |
| Abstract | The discovery of production rules (association rules and/or classification rules) is one of the most important tasks of data mining. The discovered knowledge is intelligible and comprehensible by experts in any field. In previous works, the authors used formal concept analysis to discover classification rules and association rules embedded in data sets. One of the difficulties the authors found is to measure the pertinence of the discovered rules. In supervised learning of classification rules, the authors used the known entropy measure. In un-supervised learning of association rules, they used the known support measure. However, some recent works have proven the insufficiency of these measures and have introduced other ones. In this paper, the authors present a bibliographic summary of many existing pertinence measures. Then, the authors present an experimental study of the behavior of these measures in order to help the users of our learning system, choosing the appropriate measure |
| Sponsorship | IEEE CPS |
| Starting Page | 780 |
| Ending Page | 784 |
| File Size | 204588 |
| Page Count | 5 |
| File Format | |
| ISBN | 0769527027 |
| DOI | 10.1109/ICDMW.2006.124 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Learning systems Sequences Supervised learning Lattices DNA Production Gain measurement Entropy Association rules Data mining |
| Content Type | Text |
| Resource Type | Article |
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