| dc.description.abstract | Association rule mining is one of the most important algorithms in data mining for Ecommerce. It discovers associations between items in the data that satisfy user-specified
thresholds including minimum support and minimum confidence. The application of onesize-fit-all minimum support in the algorithm, however, cannot cover all products because
different items have different purchasing frequencies, leading to the loss of many rare items
in the output. A solution to this problem is to use multiple minimum supports for items.
Yet, this approach has only been studied on Apriori and FP-Growth, which are the most
popular algorithms of association rules mining. Our paper applies this approach on the rule
generator of Classification Based on Association (CBA-RG), an associative classification
algorithm developed based on Apriori, and proposes an improved model with multiple
minimum supports called MS-CBA. For classifying the data, we apply ABC classification.
In the evaluation, our system achieves fast and stable performance, high efficiency for all
data sizes, and outperforms the classic CBA-RG in the number of rare items covered, the
number of ruleitems being generated and computational time. | en_US |
