Survey on using constraints in data mining
| Autor: | Andrea Romei, Valerio Grossi, Franco Turini |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Process (engineering)
Computer science Computer Networks and Communications Concept mining 02 engineering and technology Machine learning computer.software_genre Background knowledge Knowledge extraction 020204 information systems Constraints Data mining Information Systems Computer Science Applications1707 Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering Information system Cluster analysis Constraint (mathematics) Data stream mining business.industry Constraint satisfaction Computer Science Applications 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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