Maximal co-occurrence nonoverlapping sequential rule mining

TL;DR

This paper introduces MCoR-Miner, an efficient algorithm for mining maximal co-occurrence nonoverlapping sequential rules, improving support calculation, pruning, and candidate reduction to enhance recommendation performance.

Contribution

The paper presents MCoR-Miner, a novel algorithm that efficiently mines maximal co-occurrence nonoverlapping sequential rules with multiple optimization strategies, outperforming existing methods.

Findings

MCoR-Miner outperforms 10 other algorithms in experiments.

It yields better recommendation performance than frequent co-occurrence pattern mining.

The algorithm effectively reduces computational costs through pruning and screening strategies.

Abstract

The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given se-quence. Although various SPM methods have been investigated, most of these focus on mining all of the patterns. However, users sometimes want to mine patterns with the same specific prefix pattern, called co-occurrence pattern. Since sequential rule mining can make better use of the results of SPM, and obtain better recommendation performance, this paper addresses the issue of maximal co-occurrence nonoverlapping sequential rule (MCoR) mining and proposes the MCoR-Miner algo-rithm. To improve the efficiency of support calculation, MCoR-Miner employs depth-first search and backtracking strategies equipped with an indexing mechanism to avoid the use of sequential searching. To obviate useless support calculations for some sequences, MCoR-Miner adopts a filtering strategy to prune the sequences without the prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies the frequent item and binomial enumeration tree strategies. To avoid searching for the maximal rules through brute force, MCoR-Miner uses a screening strategy. To validate the per-formance of MCoR-Miner, eleven competitive algorithms were conducted on eight sequences. Our experimental results showed that MCoR-Miner outperformed other competitive algorithms, and yielded better recommendation performance than frequent co-occurrence pattern mining. All algorithms and datasets can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/MCoR-Miner.