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| Content Provider | Springer Nature Link |
|---|---|
| Author | Zbidi, Naim Faiz, Sami Limam, Mohamed |
| Copyright Year | 2006 |
| Abstract | Knowledge discovery in databases is used to discover useful and understandable knowledge from large databases. A process of knowledge discovery consists of two steps, the data mining step and the evaluation step. In this paper, evaluating and ranking the interestingness of summaries generated from databases, which is a part of the second step, is studied using diversity measures. Sixteen previously analyzed diversity measures of interestingness are used along with three not previously considered ones, brought from different well-known areas. The latter three measures are evaluated theoretically according to five principles that a measure must satisfy to be qualified acceptable for ranking summaries. A theoretical correlation study between the eight measures that satisfy all five principles is presented based on mathematical proofs. An empirical evaluation is conducted using three real databases. Then, a classification of the eight measures is deduced. The resulting classification is used to reduce the number of measures to only two, which are the best over all criteria, and that produce non-similar results. This helps the user interpret the most important discovered knowledge in his decision making process. |
| Starting Page | 175 |
| Ending Page | 198 |
| Page Count | 24 |
| File Format | |
| ISSN | 08856125 |
| Journal | Machine Learning |
| Volume Number | 62 |
| Issue Number | 3 |
| e-ISSN | 15730565 |
| Language | English |
| Publisher | Kluwer Academic Publishers |
| Publisher Date | 2006-01-29 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | data mining diversity measures association rules Artificial Intelligence (incl. Robotics) Automation and Robotics Computing Methodologies Simulation and Modeling Language Translation and Linguistics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Software |
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