Detail publikace

MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns

Originální název

MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns

Anglický název

MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

BibTex


@inproceedings{BUT104515,
  author="Michal {Šebek} and Martin {Hlosta} and Jaroslav {Zendulka} and Tomáš {Hruška}",
  title="MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns",
  annote="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.",
  address="Springer Verlag",
  booktitle="9th International Conference, ADMA 2013",
  chapter="104515",
  doi="10.1007/978-3-642-53914-5_14",
  edition="Lecture Notes in Computer Science",
  howpublished="print",
  institution="Springer Verlag",
  year="2013",
  month="december",
  pages="157--168",
  publisher="Springer Verlag",
  type="conference paper"
}