ALPINE: Progressive itemset mining with definite guarantees

Abstract

With increasing demand for efficient data analysis, execution time of itemset mining becomes critical for many large-scale or time-sensitive applications. We propose a dynamic approach for itemset mining that allows us to achieve flexible trade-offs between efficiency and completeness. ALPINE is to our knowledge the first algorithm to progressively mine itemsets and closed itemsets "support-wise". It guarantees that all itemsets with support exceeding the current checkpoint's support have been found before it proceeds further. Thus, it is very attractive for extremely long mining tasks with very high dimensional data because it can offer intermediate meaningful and complete results. This feature is the most important contribution of ALPINE, which is also fast but not necessarily the fastest algorithm around. Another critical advantage of ALPINE is that it does not require the apriori decided minimum support threshold.