Abstract
Frequent itemset (or frequent pattern) mining is a technique used in big data mining to discover frequently occurring sets of items (such as popular co-purchased merchandise) and has numerous applications in the field of databases. Traditional frequent pattern mining algorithms only look at Boolean mining; that is, considering only the presence or absence of an item in an itemset. In this paper, we present an algorithm for mining interesting quantitative frequent patterns. Our qEclat (or Q-Eclat) algorithm extends the common Eclat algorithm to be able to vertically mine quantitative patterns. When compared with the existing MQA-M algorithm (which was built for quantitative horizontal frequent pattern mining), our evaluation results show that qEclat mines quantitative frequent patterns faster.
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