@inproceedings{14f792f05f5d4112be28a393e1a6c2aa,
title = "Efficient mining of time interval-based association rules",
abstract = "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.",
keywords = "Association rule mining, Time-interval association rule",
author = "Lee, \{Ki Yong\} and Suh, \{Young Kyoon\}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 4th International Conference on Big Data Applications and Services, BigDAS 2017 ; Conference date: 15-08-2017 Through 18-08-2017",
year = "2019",
doi = "10.1007/978-981-13-0695-2\_13",
language = "English",
isbn = "9789811306945",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "121--125",
editor = "Leung, \{Carson K.\} and Wookey Lee",
booktitle = "Big Data Applications and Services 2017 - The 4th International Conference on Big Data Applications and Services",
address = "Germany",
}