DiffNodesets: An Efficient Structure for Fast Mining Frequent Itemsets

TL;DR

This paper introduces DiffNodeset, a new efficient data structure for mining frequent itemsets, and presents the dFIN algorithm that outperforms existing methods in speed through extensive experiments.

Contribution

Proposes DiffNodeset, a novel itemset representation, and the dFIN algorithm, which efficiently mines frequent itemsets without candidate generation in many cases.

Findings

dFIN is significantly faster than existing algorithms.

DiffNodeset improves mining efficiency over previous node set-based methods.

Extensive experiments validate the superior performance of dFIN.

Abstract

Mining frequent itemsets is an essential problem in data mining and plays an important role in many data mining applications. In recent years, some itemset representations based on node sets have been proposed, which have shown to be very efficient for mining frequent itemsets. In this paper, we propose DiffNodeset, a novel and more efficient itemset representation, for mining frequent itemsets. Based on the DiffNodeset structure, we present an efficient algorithm, named dFIN, to mining frequent itemsets. To achieve high efficiency, dFIN finds frequent itemsets using a set-enumeration tree with a hybrid search strategy and directly enumerates frequent itemsets without candidate generation under some case. For evaluating the performance of dFIN, we have conduct extensive experiments to compare it against with existing leading algorithms on a variety of real and synthetic datasets. The experimental results show that dFIN is significantly faster than these leading algorithms.