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.

Frequent pattern mining is a popular technique in big data mining and analytics. It discovers frequently occurring sets of items (e.g., popular merchandise items, frequently co-occurring events) from big data found in numerous database engineered applications. These frequent patterns can be discovered horizontally by transaction-centric mining algorithms or vertically by item-centric mining algorithms. Regardless of their mining direction (horizontal or vertical), traditional frequent pattern mining algorithms aim to discover Boolean frequent patterns in the sense that patterns capture the presence (or absence) of items within the discovered patterns. However, there are many real-life situations, in which quantities of items within the patterns are important. For example, the quantity of items may also affect profits of selling the items within the discovered patterns. Hence, in this paper, we present an algorithm for vertical mining of interesting quantitative frequent patterns. This Q-Eclat algorithm first represents the big data as a collection of equivalence classes according to their prefix item labels. Each domain item is represented by one of these classes. Their corresponding item-centric sets capture (a) IDs of transactions containing the item, as well as (b) the quantity of that item in each transaction. With this representation, our algorithm then vertically mines quantitative frequent patterns. When compared the existing MQA-M algorithm (which was built for quantitative horizontal frequent pattern mining), evaluation results show that our quantitative vertical Q-Eclat algorithm takes shorter runtime to mine quantitative frequent patterns.