@inproceedings{8d4267e697404721a7e85a285847c6cd,
title = "Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences",
abstract = "The need to analyze information from streams arises in a variety of applications. One of its fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the presence of the pattern in transactions but pay no attention to the series of itemsets and their multiple occurrences. The pattern over a window of itemsets stream and their multiple occurrences, however, provides additional capability to recognize the essential characteristics of the patterns and the inter-relationships among them that are unidentifiable by the existing presence-based studies. In this paper, we study such a new sequential pattern mining problem and propose a corresponding sequential miner with novel strategies to prune the search space efficiently. Experiments on both real and synthetic data show the utility of our approach.",
keywords = "Event sequence, Multiple occurrences, Serial episode",
author = "Thomas Guyet and Wenbin Zhang and Albert Bifet",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 22nd Annual International Conference on Computational Science, ICCS 2022 ; Conference date: 21-06-2022 Through 23-06-2022",
year = "2022",
month = jan,
day = "1",
doi = "10.1007/978-3-031-08751-6\_33",
language = "English",
isbn = "9783031087509",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "460--472",
editor = "Derek Groen and \{de Mulatier\}, Cl{\'e}lia and Krzhizhanovskaya, \{Valeria V.\} and Sloot, \{Peter M.A.\} and Maciej Paszynski and Dongarra, \{Jack J.\}",
booktitle = "Computational Science - ICCS 2022, 22nd International Conference, Proceedings",
}