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| Content Provider | Springer Nature Link |
|---|---|
| Author | Grossi, Valerio Romei, Andrea Turini, Franco |
| Copyright Year | 2016 |
| Abstract | This paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints in a data mining task requires specific definition and satisfaction tools during knowledge extraction. This survey proposes three groups of studies based on classification, clustering and pattern mining, whether the constraints are on the data, the models or the measures, respectively. We consider the distinctions between hard and soft constraint satisfaction, and between the knowledge extraction phases where constraints are considered. In addition to discussing how constraints can be used in data mining, we show how constraint-based languages can be used throughout the data mining process. |
| Starting Page | 424 |
| Ending Page | 464 |
| Page Count | 41 |
| File Format | |
| ISSN | 13845810 |
| Journal | Data Mining and Knowledge Discovery |
| Volume Number | 31 |
| Issue Number | 2 |
| e-ISSN | 1573756X |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2016-10-22 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Data mining Constraints Background knowledge Data Mining and Knowledge Discovery Artificial Intelligence (incl. Robotics) Information Storage and Retrieval Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications Information Systems Computer Science Applications |
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