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. Copyright 2006 ACM.
Mining sequences with temporal annotations / Giannotti, F.; Nanni, M.; Pedreschi, D.; Pinelli, F.. - 1:(2006), pp. 593-597. ( 2006 ACM Symposium on Applied Computing Dijon, fra 2006) [10.1145/1141277.1141413].
Mining sequences with temporal annotations
Nanni M.;Pinelli F.
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. Copyright 2006 ACM.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
