The Rule Extraction of Numerical Association Rule Mining Using Hybrid Evolutionary Algorithm
نویسندگان
چکیده
منابع مشابه
Optimization of Spatial Association Rule Mining using Hybrid Evolutionary algorithm
Spatial data refer to any data about objects that occupy real physical space. Attributes within spatial databases usually include spatial information. Spatial data refers to the numerical or categorical values of a function at different spatial locations. Spatial metadata refers to the descriptions of the spatial configuration. Application of classical association rule mining concepts to spatia...
متن کاملOptimization of Spatial Association Rule Mining using Hybrid Evolutionary Algorithm
Spatial data refer to any data about objects that occupy real physical space. Attributes within spatial databases usually include spatial information. Spatial data refers to the numerical or categorical values of a function at different spatial locations. Spatial metadata refers to the descriptions of the spatial configuration. Application of classical association rule mining concepts to spatia...
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Most of the approaches for association rule mining focus on the performance of the discovery of the frequent itemsets. They are based on the algorithms that require to transform data from one representation to another, and therefore excessively use resource and incur heavy CPU overhead. This paper proposes a hybrid algorithm that is a resource-efficient and provides better performance. It chara...
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Generally association rule mining (ARM) algorithms, like the apriori algorithm, initial produce frequent itemsets and afterward, from the frequent itemsets, the association rules that go beyond the minimum confidence threshold. When the data is in large volume, it takes number of scans to generate frequent items.It is a better idea if all the association rules generated directly without generat...
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ژورنال
عنوان ژورنال: Proceeding of the Electrical Engineering Computer Science and Informatics
سال: 2017
ISSN: 2407-439X,2407-439X
DOI: 10.11591/eecsi.v4.1083