Publication detail

MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns

ŠEBEK, M. HLOSTA, M. ZENDULKA, J. HRUŠKA, T.

Original Title

MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns

English Title

MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

closed sequential pattern mining,taxonomy,generalization,GSP,MLSP

RIV year

2013

Released

14.12.2013

Publisher

Springer Verlag

Location

Hangzhou

ISBN

978-3-642-53913-8

Book

9th International Conference, ADMA 2013

Edition

Lecture Notes in Computer Science

Edition number

NEUVEDEN

Pages from

157

Pages to

168

Pages count

12

URL

Documents

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"
}