Logics for representing data mining tasks in inductive databases
| Autor: | Jixue Liu, Jiuyong Li, Hong-Cheu Liu, Millist W. Vincent |
|---|---|
| Přispěvatelé: | Liu, Hong-Cheu, Vincent, Millist, Liu, Jixue, Li, Jiuyong, 25th Australasian Database Conference, ADC 2014 Brisbane, Australia 14-16 July 2014 |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Database
Association rule learning Computer science InformationSystems_DATABASEMANAGEMENT data mining computer.software_genre Query language Expressive power Variety (cybernetics) Logical framework inductive databases ComputingMethodologies_PATTERNRECOGNITION logical framework Outlier Data mining Cluster analysis computer Logic programming |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319086071 ADC |
| Popis: | We present a logical framework for querying inductive databases, which can accommodate a variety of data mining tasks, such as classification, clustering, finding frequent patterns and outliers detection. We also address the important issues of the expressive power of inductive query languages. We show that the proposed logic programming paradigm has equivalent expressive power to an algebra for data mining presented in the literature [1]. Refereed/Peer-reviewed |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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