Survey of Pattern Mining Methods Based on Biological Heuristic Algorithms

doi:10.3778/j.issn.1002-8331.2401-0427

Abstract

Abstract: Frequent itemset mining, association rule mining and high utility itemset mining are three related and developing fields in pattern mining. In recent years, because traditional algorithms cannot cope with the explosive growth of data environment, heuristic algorithms have become a research hotspot in pattern mining methods. In order to reveal the research and development status in the field of pattern mining, firstly, the research results of frequent patterns and high utility pattern methods are comprehensively analyzed and summarized from the perspective of many biological heuristic algorithms, such as particle swarm optimization, genetic algorithm, ant colony optimization, and artificial bee colony and so on. Secondly, different biological heuristic pattern mining methods are summarized from strategy, comparison algorithm, datasets, advantages and disadvantages, and the experimental results and performance indicators of the same datasets are compared and analyzed in detail. Finally, in view of the shortcomings of the current biological heuristic pattern mining methods, the next research direction is put forward, including dynamic data flow, multi-objective evolution, fuzzy computing and complex data types.

Key words: biological heuristic, frequent itemset, association rule, high utility itemset

摘要： 频繁项集挖掘、关联规则挖掘和高效用项集挖掘是模式挖掘中相互关联且不断发展的三个领域。近年来，由于传统算法无法应对爆炸式增长的数据环境，启发式算法已成为模式挖掘方法中的研究热点。为揭示模式挖掘领域的研究与发展现状，从粒子群、遗传、蚁群、蜂群等多个生物启发式算法角度对近年的频繁模式和高效用项集方法研究成果进行全面的分析与总结。对不同生物启发的模式挖掘方法，从使用策略、对比算法、数据集、优缺点进行概述，对相同数据集的实验结果及性能指标进行详细的对比分析。针对目前生物启发式模式挖掘方法中的不足，提出了下一步的研究方向，包括动态数据流、多目标进化、模糊计算和复杂数据类型的研究。

关键词: 生物启发式, 频繁项集, 关联规则, 高效用项集

HAN Meng, HE Feifei, ZHANG Ruihua, LI Chunpeng, MENG Fanxing. Survey of Pattern Mining Methods Based on Biological Heuristic Algorithms[J]. Computer Engineering and Applications, 2024, 60(16): 19-33.

韩萌, 何菲菲, 张瑞华, 李春鹏, 孟凡兴. 生物启发式的模式挖掘方法综述[J]. 计算机工程与应用, 2024, 60(16): 19-33.

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