Improved Genetic Algorithm for High-Utility Itemset Mining

Abstract

High-utility itemset mining (HUIM) is an important research topic in the data mining field. Typically, traditional HUIM algorithms must handle the exponential problem of huge search space when the database size or number of distinct items is very large. As an alternative and effective approach, evolutionary computation (EC)-based algorithms have been proposed to solve HUIM problems because they can obtain a set of nearly optimal solutions in limited time. However, it is still time-consuming for EC-based algorithms to find complete high-utility itemsets (HUIs) in transactional databases. To address this problem, we propose an HUIM algorithm based on an improved genetic algorithm (HUIM-IGA). In addition, a neighborhood exploration strategy is proposed to improve search efficiency for HUIs. To reduce missing HUIs, a population diversity maintenance strategy is employed in the proposed HUIM-IGA. An individual repair method is also introduced to reduce invalid combinations for discovering HUIs. In addition, an elite strategy is employed to prevent the loss of HUIs. Experimental results obtained on a set of real-world datasets demonstrate that the proposed algorithm can find complete HUIs in terms of the given minimum utility threshold, and the timeconsuming of HUIM-IGA is relatively lower when mining the same number of HUIs than state-of-the-art EC-based HUIM algorithms.INDEX TERMS Data mining, high-utility itemset mining, genetic algorithm, neighborhood exploration, diversity maintenance. of the Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangsu. His major research areas and work are related to computational intelligence, machine learning, bioinformatics, and among others. He published more than 150 articles in journals, conference proceedings, and several books in the above areas.

A High Utility Itemset Mining Algorithm Based on Particle Filter

“…Unlike the HUIM algorithms described above, this paper designs a particle flter-based HUIM method that uses sampling to flter out HUIs in a dataset. Among the numerous research studies on HUIM algorithms, the heuristicbased HUIM algorithms [26][27][28][29][30][31][32][33][34][35][36][37][38] are the most relevant to us. Inspired by the heuristic methods [39,40], heuristic-based HUIM algorithms frst generate random initial candidates, then update the candidates using behavioral patterns of natural organisms, and fnally, flter out the HUIs from the candidates.…”

An Efficient Approach for Mining Reliable High Utility Patterns

“…Utility-based mining is proposed to determine all patterns that satisfy a minimum utility threshold. The classical HUPM uses utility measurement as the sole interestingness measure to evaluate the importance of itemsets, such as HUI-Miner [12], HUIM-IGA [4], EFIM [13], HIMU [14], and CBPM [15]. These algorithms are similar in input and output, with only differences in the data structures or strategies applied for reducing search space, memory consumption, and runtime.…”

“…High Utility Itemset Mining (HUIM) refers to extracting all itemsets that exceed a predefined minimum utility threshold minUtil set by the user using a utility function [4], [5]. HUIM has been extensively used for process model extraction in different applications, such as recommendation systems, retail market analysis, and medical applications [6], [2].…”

Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms

“…A GA is an optimization algorithm based on the principle of biological scientific selection. GAs are widely used in linear and nonlinear optimization problems [15,33,34]. In this study, the inversion of Okada's model is also carried out through an optimization approach using GAs.…”