Publication detail

Multi-level Sequence Mining Based on GSP

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

Original Title

Multi-level Sequence Mining Based on GSP

English Title

Multi-level Sequence Mining Based on GSP

Type

journal article - other

Language

en

Original Abstract

Mining sequential patterns is an important problem in the field of data mining and many algorithms and optimization techniques have been published to deal with that problem. The GSP algorithm, which is one of them, can be used for mining sequential patterns with some additional constraints. In this paper, we propose a new algorithm for mining multi-level sequential patterns based on GSP. The idea is that if a more general item appears in a pattern, the pattern has higher or at least the same support as the one containing the corresponding specific item. However, too generalized sequence patterns are not important for user. In our algorithm generalization uses a selective method based on information content of patterns. This allows us to mine more patterns with the same minimal support threshold and to reveal new potentially useful patterns.

English abstract

Mining sequential patterns is an important problem in the field of data mining and many algorithms and optimization techniques have been published to deal with that problem. The GSP algorithm, which is one of them, can be used for mining sequential patterns with some additional constraints. In this paper, we propose a new algorithm for mining multi-level sequential patterns based on GSP. The idea is that if a more general item appears in a pattern, the pattern has higher or at least the same support as the one containing the corresponding specific item. However, too generalized sequence patterns are not important for user. In our algorithm generalization uses a selective method based on information content of patterns. This allows us to mine more patterns with the same minimal support threshold and to reveal new potentially useful patterns.

Keywords

multi-level sequence pattern mining, GSP, taxonomy

RIV year

2012

Released

10.10.2012

Publisher

NEUVEDEN

Location

NEUVEDEN

Pages from

31

Pages to

38

Pages count

8

URL

BibTex


@article{BUT96928,
  author="Michal {Šebek} and Martin {Hlosta} and Jan {Kupčík} and Jaroslav {Zendulka} and Tomáš {Hruška}",
  title="Multi-level Sequence Mining Based on GSP",
  annote="Mining sequential patterns is an important problem in the field of data mining
and many algorithms and optimization techniques have been published to deal with
that problem. The GSP algorithm, which is one of them, can be used for mining
sequential patterns with some additional constraints. In this paper, we propose
a new algorithm for mining multi-level sequential patterns based on GSP. The idea
is that if a more general item appears in a pattern, the pattern has higher or at
least the same support as the one containing the corresponding specific item.
However, too generalized sequence patterns are not important for user. In our
algorithm generalization uses a selective method based on information content of
patterns. This allows us to mine more patterns with the same minimal support
threshold and to reveal new potentially useful patterns.",
  address="NEUVEDEN",
  chapter="96928",
  doi="10.2478/v10198-012-0012-8",
  edition="NEUVEDEN",
  howpublished="print",
  institution="NEUVEDEN",
  number="2",
  volume="2012",
  year="2012",
  month="october",
  pages="31--38",
  publisher="NEUVEDEN",
  type="journal article - other"
}