Frequentness-Transition Queries for Distinctive Pattern Mining from Time-Segmented Databases
Shin-ichi Minato, Takeaki Uno · 2010
We propose a new data mining method called frequentness-transitional pattern mining for finding patterns with interesting sequential behavior specified by a user's query. For a series of databases, we introduce the frequentness-sequence of a pattern that is a sequence of the two symbols ‘H’ and ‘L,’ which represent the frequency or infrequency in each segment of a database, respectively. The problem is finding patterns whose frequentness-sequences satisfy the query. The goal of this research is to develop an efficient algorithm and its implementation that accepts various models and that can be widely used in practice with large-scale data. Thus, we chose an itemset as a pattern, and regular expression for the query language to accept various models. To cope with the unavoidably large number of candidate patterns, we use Zero-suppressed Binary Decision Diagrams (ZDDs or ZBDDs) to store and operate a large number of candidate itemsets in a short time. Our algorithm performed quite well in our computational experiments, such that it is competitive with the standard itemset mining algorithms that can be used only to find frequent itemsets. To the best of our knowledge, this is the first study on detecting distinctive itemsets of user-specific models of sequential behaviors.