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An Ontological Characterization of Time-Series and State-Sequences for Data Mining

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3 Author(s)
Ma, Jixin ; Univ. of Greenwich, London ; Rongfang Bie ; Guoxing Zhao

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.

Published in:

Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on  (Volume:5 )

Date of Conference:

18-20 Oct. 2008