Frequent Itemset Mining in High Dimensional Data: A Review

This paper provides a brief overview of the techniques used in frequent itemset mining. It discusses the search strategies used; i.e. depth first vs. breadth-first, and dataset representation; i.e. horizontal vs. vertical representation. In addition, it reviews many techniques used in several algorithms that make frequent itemset mining more efficient. These algorithms are discussed based on the proposed search strategies which include row-enumeration vs. column-enumeration, bottom-up vs. top-down traversal, and a number of new data structures. Finally, the paper reviews on the latest algorithms of colossal frequent itemset/pattern which currently is the most relevant to mining high-dimensional dataset.