@inproceedings{13ca7098489d4bc5a518a95842fbf0fc,
title = "Incrementally mining temporal patterns in interval-based databases",
abstract = "In several applications, sequence databases generally update incrementally with time. Obviously, it is impractical and inefficient to re-mine sequential patterns from scratch every time a number of new sequences are added into the database. Some recent studies have focused on mining sequential patterns in an incremental manner; however, most of them only considered patterns extracted from time point-based data. In this paper, we proposed an efficient algorithm, Inc-TPMiner, to incrementally mine sequential patterns from interval-based data. We also employ some optimization techniques to reduce the search space effectively. The experimental results indicate that Inc-TPMiner is efficient in execution time and possesses scalability. Finally, we show the practicability of incremental mining of interval-based sequential patterns on real datasets.",
keywords = "dynamic representation, incremental mining, interval-based pattern, sequential pattern mining",
author = "Chen, {Yi Cheng} and Weng, {Julia Tzu Ya} and Wang, {Jun Zhe} and Chou, {Chien Li} and Huang, {Jiun Long} and Lee, {Suh Yin}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014 ; Conference date: 30-10-2014 Through 01-11-2014",
year = "2014",
month = mar,
day = "10",
doi = "10.1109/DSAA.2014.7058089",
language = "???core.languages.en_GB???",
series = "DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "304--311",
editor = "George Karypis and Longbing Cao and Wei Wang and Irwin King",
booktitle = "DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics",
}