基于kestrel的关联规则挖掘与频繁变化项分类算法

I. E. Agbehadji, R. Millham, S. Fong
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引用次数: 12

摘要

受自然启发的方法已被用于为现实生活中的问题设计计算机解决方案。这些计算机解决方案采用算法的形式,描述动物或鸟类在自然栖息地的特定行为。现代两个受生物启发的计算概念包括进化和群体智能。一个新颖的介绍,以生物启发的计算概念的群体行为是研究红隼的特点。本文提出了一种基于红隼搜索算法(KSA)的元启发式算法,用于大数据环境下频繁变化项的关联规则挖掘和分类。该算法旨在利用最小支持度和最小置信度在数据集中找到可能的最佳规则和模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kestrel-Based Search Algorithm for Association Rule Mining and Classification of Frequently Changed Items
Nature inspired approaches have been used in the design of computer solutions for real life problems. These computer solutions take the form of algorithms which characterize specific behaviour of animals or birds in their natural habitat. The two bio-inspired computational concepts in modern times includes evolutionary and swarm intelligence. A novel introduction to the bio-inspired computational concepts of swarm behaviour is the study of characteristics of kestrel birds. The study presents, as a concept paper, a meta-heuristic algorithm called kestrel-based search algorithm (KSA) for association rule mining and classification of frequently changed items on big data environment. This algorithm aims to find best possible rules and patterns in dataset using minimum support and minimum confidence.
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