A CP-based approach for mining sequential patterns with quantities
This paper addresses the problem of mining sequential patterns (SPM) from data represented as a set of sequences. In this work, we are interested in sequences of items in which each item is associated with its quantity. To the best of our knowledge, existing approaches don’t allow to handle this ki...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asociación Española para la Inteligencia Artificial
2023-03-01
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| Series: | Inteligencia Artificial |
| Subjects: | |
| Online Access: | https://journal.iberamia.org/index.php/intartif/article/view/954 |
| Summary: | This paper addresses the problem of mining sequential patterns (SPM) from data represented as a set of
sequences. In this work, we are interested in sequences of items in which each item is associated with its quantity.
To the best of our knowledge, existing approaches don’t allow to handle this kind of sequences under constraints.
In the other hand, several proposals show the efficiency of constraint programming (CP) to solve SPM problem
dealing with several kind of constraints. However, in this paper, we propose the global constraint QSPM which
is an extension of the two CP-based approaches proposed in [5] and [7]. Experiments on real-life datasets show
the efficiency of our approach allowing to specify many constraints like size, membership and regular expression
constraints.
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| ISSN: | 1137-3601 1988-3064 |