THE BASES OF ASSOCIATION RULES OF HIGH CONFIDENCE
| Autor: | Anuar Sharafudinov, Justin Cabot-Miller, Oren Segal, Kira Adaricheva, James B. Nation |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Association rule learning Basis (linear algebra) Computer science parallel computing Databases (cs.DB) computer.software_genre Association rules Set (abstract data type) implication Ranking Computer Science - Databases Table (database) Relevance (information retrieval) binary table Data mining D-basis Transaction data computer Row |
| Popis: | We develop a new approach for distributed computing of the association rules of high confidence in a binary table. It is derived from the D-basis algorithm in K. Adaricheva and J.B. Nation (TCS 2017), which is performed on multiple sub-tables of a table given by removing several rows at a time. The set of rules is then aggregated using the same approach as the D-basis is retrieved from a larger set of implications. This allows to obtain a basis of association rules of high confidence, which can be used for ranking all attributes of the table with respect to a given fixed attribute using the relevance parameter introduced in K. Adaricheva et al. (Proceedings of ICFCA-2015). This paper focuses on the technical implementation of the new algorithm. Some testing results are performed on transaction data and medical data. Presented at DTMN, Sydney, Australia, July 28, 2018 |
| Databáze: | OpenAIRE |
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