关联规则挖掘研究综述

Surbhi K. Solanki, Jalpa Patel
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引用次数: 76

摘要

从大数据中提取有用的和有趣的知识的任务被称为数据挖掘。它包括聚类、分类、关联挖掘、离群点检测、回归等多个方面。其中,关联规则挖掘是数据挖掘的一个重要方面。关联规则挖掘的最佳示例是市场购物篮分析。关联规则挖掘的应用领域包括股票分析、web日志挖掘、医疗诊断、客户市场分析、生物信息学等。过去,研究者们开发了许多布尔和模糊关联规则挖掘算法,如Apriori、FP-tree、Fuzzy FP-tree等。我们将在本文后面的部分详细讨论它们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey on Association Rule Mining
Task of extracting useful and interesting knowledge from large data is called data mining. It has many aspects like clustering, classification, association mining, outlier detection, regression etc. Among them association rule mining is one of the important aspect for data mining. Best example of association rule mining is market-basket analysis. Applications of association rule mining are stock analysis, web log mining, medical diagnosis, customer market analysis bioinformatics etc. In past, many algorithms were developed by researchers for Boolean and Fuzzy association rule mining such as Apriori, FP-tree, Fuzzy FP-tree etc. We are discussing them in detail in later section of this paper.
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