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Journal article
Using a cosine-type measure to derive strong association mining rules
International Journal of Knowledge Engineering and Data Mining, Vol.1(1), pp.69-83
01/01/2010
Abstract
Association mining rule algorithms have two major drawbacks - the need to repeatedly scan the dataset and the generation of too many association rules. In this paper we present an algorithm that concentrates on addressing these drawbacks. We present a correlation based association mining rule algorithm, implemented using an arraylist structure in JAVA, that does not require more than one scan of the full dataset and generates far lot less strong association mining rules. The correlation criteria used is a cosine-type measure.
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Details
- Title
- Using a cosine-type measure to derive strong association mining rules
- Publication Details
- International Journal of Knowledge Engineering and Data Mining, Vol.1(1), pp.69-83
- Resource Type
- Journal article
- Publisher
- Inderscience Publishers
- Copyright
- © Inderscience Publishers
- Identifiers
- 99380178996906600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Mathematics and Statistics; Computer Science
- Language
- English