Abstract
Sequence mining, a branch of data mining, is recently an important research area, which recognizes subsequences repeated in a temporal database. Fuzzy sequence mining can express the problem as quality form that leads to more desirable results. Sequence mining algorithms focus on the items with support higher than a specified threshold. Considering items with similar mental concepts lead to general and more compact sequences in database which might be indistinguishable in situations where the support of individual items are less than threshold. This paper proposes an algorithm to find sequences with more general concepts by considering mental similarity between items by the use of fuzzy ontology.
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Gholizadeh, M., Pedram, M.M., Shanbezadeh, J. (2012). Mining General Fuzzy Sequences Based on Fuzzy Ontology. In: Ao, S., Castillo, O., Huang, X. (eds) Intelligent Control and Innovative Computing. Lecture Notes in Electrical Engineering, vol 110. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1695-1_20
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DOI: https://doi.org/10.1007/978-1-4614-1695-1_20
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