In this paper we propose an extension of the sequence mining paradigm to (temporally-)annotated sequential patterns, where each transition in a sequential pattern is annotated with a typical transition time derived from the source data. Then, we present a basic solution for the novel mining problem based on the combination of sequential pattern mining and clustering, and assess this solution on two realistic datasets, illustrating how potentially useful patterns of the new form are extracted.

Mining sequences with temporal annotations

Giannotti F;Nanni M;
2006

Abstract

In this paper we propose an extension of the sequence mining paradigm to (temporally-)annotated sequential patterns, where each transition in a sequential pattern is annotated with a typical transition time derived from the source data. Then, we present a basic solution for the novel mining problem based on the combination of sequential pattern mining and clustering, and assess this solution on two realistic datasets, illustrating how potentially useful patterns of the new form are extracted.
2006
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
Inglese
ACM Symposium on Applied Computing - Special Track on Data Mining
593
597
9
1-59593-108-2
Sì, ma tipo non specificato
23-27/04/2006
Dijon, France
Temporal Data Mining
Sequential Patterns
Codice Puma: cnr.isti/2006-A2-17
2
restricted
Giannotti F.; Nanni M.; Pedreschi D.; Pinelli F.
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/62255
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