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Discovering Knowledge about Key Sequences for Indexing Time Series Cases
Mälardalen University, Department of Computer Science and Electronics.ORCID iD: 0000-0002-5562-1424
Mälardalen University, Department of Computer Science and Electronics.ORCID iD: 0000-0001-9857-4317
2006 (English)In: Advances in Case-Based Reasoning: 8th European Conference, ECCBR 2006 Fethiye, Turkey, September 4-7, 2006 Proceedings, 2006, p. 474-488Conference paper, Published paper (Refereed)
Abstract [en]

Coping with time series cases is becoming an important issue in case based reasoning. This paper develops a knowledge discovery approach to discovering significant sequences for depicting symbolic time series cases. The input is a case library containing time series cases consisting of consecutive discrete patterns. The proposed approach is able to find from the given case library all qualified sequences that are non-redundant and indicative. A sequence as such is termed as a key sequence. It is shown that the key sequences discovered are highly usable in case characterization to capture important properties while ignoring random trivialities. The main idea is to transform an original (lengthy) time series into a more concise representation in terms of the detected occurrences of key sequences. Three alternate ways to develop case indexes based on key sequences are suggested. These indexes are simply vectors of numbers that are easily usable when matching two time series cases for case retrieval.

Place, publisher, year, edition, pages
2006. p. 474-488
Series
Lecture Notes in Artificial Intelligence ; 4106
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-6926DOI: 10.1007/11805816_35ISBN: 978-3-540-36846-5 (print)OAI: oai:DiVA.org:mdh-6926DiVA, id: diva2:236936
Conference
8th European Conference, ECCBR 2006 Fethiye, Turkey, September 4-7, 2006
Available from: 2009-09-25 Created: 2009-09-25 Last updated: 2015-09-15Bibliographically approved

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Funk, PeterXiong, Ning

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CiteExportLink to record
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  • apa
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  • nn-NB
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