Efficient Mining of Time Interval-Based Association Rules

被引：1

作者：

Lee, Ki Yong ^{[1
]}

Suh, Young-Kyoon ^{[2
]}

机构：

[1] Sookmyung Womens Univ, Div Comp Sci, Seoul 04310, South Korea

[2] Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea

来源：

BIG DATA APPLICATIONS AND SERVICES 2017 | 2019年 / 770卷

基金：

新加坡国家研究基金会;

关键词：

Association rule mining; Time-interval association rule;

D O I：

10.1007/978-981-13-0695-2_13

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Given market or log data, it is very useful to find two sets of items or events that occur frequently with a regular time interval. We call a time-dependent relationship between two itemsets a time interval-based association rule. Finding time interval-based association rules, however, has not been much investigated yet until now. In this paper, we propose an efficient method for finding time interval-based association rules. The proposed method transforms the original input data into a more efficient form and then utilizes the transformed data in the subsequent steps. As a result, the input/output (I/O) cost of reading the data from disk is significantly reduced. Our experiments demonstrate the efficiency of the proposed method compared with those of the existing methods.

引用

页码：121 / 125

页数：5