Mining N-most Interesting Multi-level Frequent Itemsets without Support Threshold

Abstract

Mining multi-level frequent itemsets from transactional database is one of the most important tasks in data mining community. It aims to discover correlation among items with their hierarchical categories under support-confidence values and thresholds. However, it is well-known that the task of providing an appropriate support threshold to mine the most interesting patterns without prior knowledge in advance is very difficult and it is more reasonable to ask the users to specify the number of desired patterns. Therefore, in this paper, we propose an alternative approach to mine the most interesting multi-level frequent patterns without the setting of support threshold, called N-most interesting multi-level frequent pattern mining, where N is the number of desired patterns with the highest support values per each category level. To mine such patterns, an efficient adaptive FP-growth algorithm, called NMLFP, is proposed. Extensive performance studies show that NMLFP has high performance and linearly scalable on the number of desired results.