Mining of Frequent Action Rules

被引：7

作者：

Dardzinska, Agnieszka ^{[1
]}

Romaniuk, Anna ^{[1
]}

机构：

[1] Bialystok Tech Univ, Dept Mech & Comp Sci, Ul Wiejska 45a, PL-15351 Bialystok, Poland

来源：

MACHINE INTELLIGENCE AND BIG DATA IN INDUSTRY | 2016年 / 19卷

关键词：

Action rules; Association; Action base; FP-growth; Frequent action tree; Decision system; Information system; Diabetic;

D O I：

10.1007/978-3-319-30315-4_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An action rule is constructed as a series of changes, or actions, which can be made to some of the flexible characteristics of the information system that ultimately triggers a change in the targeted attribute. The existing action rules discovery methods consider the input decision system as their search domain and are limited to expensive and ambiguous strategies. In this paper, we define and propose the notion of action base as the search domain for actions, and then propose a strategy based on the FP-Growth algorithm to achieve high performance in action rules extraction. This method was initially tested on real medical diabetic database. The obtained results are quite promising.

引用

页码：87 / 95

页数：9

共 12 条

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