An Association Rule Mining Algorithm Based on Multiple Supports

Abstract

Abstract: Association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rule) in the data that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf) constraints. In order to avoid rare item problem, Liu Bing proposed a model with multiple minimum supports. This paper proposes a new algorithm based on the multiple minimum model.

摘要： 关联规则采集是数据采集中的一类重要模型。规则采集算法用来发现数据中所有满足用户指定的最小支持度和最小可信度的子项关联（即规则）。国外某些学者提出了一个多支持度的模型，解决了单支持度模型中可能出现的稀有子项问题。基于该多支持度的模型提出了一种新的数据采集算法。

关键词: 数据采集, 关联规则, 最小可信度, 最小支持度, 大子项集

CLC Number:

TP311.13

LOU Xiaohong, DING Baokang. An Association Rule Mining Algorithm Based on Multiple Supports[J]. , 2001, 27(06): 102-103.

楼晓鸿,丁宝康. 一种多支持度的关联规则采集算法[J]. , 2001, 27(06): 102-103.

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URL: https://www.ecice06.com/EN/Y2001/V27/I06/102

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