Publication Details
MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns
Hlosta Martin, Ing., Ph.D.
Zendulka Jaroslav, doc. Ing., CSc. (UIFS)
Hruška Tomáš, prof. Ing., CSc. (DIFS)
closed sequential pattern mining,taxonomy,generalization,GSP,MLSP
The problem of mining sequential patterns has been widely studied and many
efficient algorithms used to solve this problem have been published. In some
cases, there can be implicitly or explicitely defined taxonomies (hierarchies)
over input items (e.g. product categories in a e-shop or sub-domains in the DNS
system). However, how to deal with taxonomies in sequential pattern mining is
marginally discussed. In this paper, we formulate the problem of mining
hierarchically-closed multi-level sequential patterns and demonstrate its
usefulness. The MLSP algorithm based on the on-demand generalization that
outperforms other similar algorithms for mining multi-level sequential patterns
is presented here.
@inproceedings{BUT104515,
author="Michal {Šebek} and Martin {Hlosta} and Jaroslav {Zendulka} and Tomáš {Hruška}",
title="MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns",
booktitle="9th International Conference, ADMA 2013",
year="2013",
series="Lecture Notes in Computer Science",
pages="157--168",
publisher="Springer Verlag",
address="Hangzhou",
doi="10.1007/978-3-642-53914-5\{_}14",
isbn="978-3-642-53913-8",
url="http://link.springer.com/chapter/10.1007/978-3-642-53914-5_14"
}