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An ontological characterization of time-series and state-sequences for data mining

An ontological characterization of time-series and state-sequences for data mining

Ma, Jixin, Bie, Rongfang and Zhao, Guoxing (2008) An ontological characterization of time-series and state-sequences for data mining. Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Institute of Electrical and Electronics Engineers, Inc., Piscataway, NJ, USA, pp. 325-329. ISBN 978-0-7695-3305-6 (doi:https://doi.org/10.1109/FSKD.2008.2)

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Abstract

Time-series and sequences are important patterns in data mining. Based on an ontology of time-elements, this paper presents a formal characterization of time-series and state-sequences, where a state denotes a collection of data whose validation is dependent on time. While a time-series is formalized as a vector of time-elements temporally ordered one after another, a state-sequence is denoted as a list of states correspondingly ordered by a time-series. In general, a time-series and a state-sequence can be incomplete in various ways. This leads to the distinction between complete and incomplete time-series, and between complete and incomplete state-sequences, which allows the expression of both absolute and relative temporal knowledge in data mining.

Item Type: Book Section
Additional Information: This paper forms part of the Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, held in Jinan Shandong, China, 18-20 October 2008. ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Uncontrolled Keywords: ontology, time-series, data mining, state-sequence
Subjects: Q Science > QA Mathematics
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Computer & Computational Science Research Group
School of Computing & Mathematical Sciences > Department of Computer Science
Related URLs:
Last Modified: 14 Oct 2016 09:03
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/1234

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