Are Zero-suppressed Binary Decision Diagrams Good for Mining Frequent Patterns in High Dimensional Datasets?
Mining frequent patterns such as frequent itemsets is a core operation in many important data mining tasks, such as in association rule mining. Mining frequent itemsets in high dimensional datasets is challenging, since the search space is exponential in the number of dimensions and the volume of patterns can be huge. Many of the state-of-the-art techniques rely upon the use of prefix trees (e.g. FP- trees) which allow nodes to be shared among common prefix paths. However, the scalability of such techniques may be limited when handling high dimensional datasets.