Detail publikace

Generator of Synthetic Datasets for Hierarchical Sequential Pattern Mining Evaluation

Originální název

Generator of Synthetic Datasets for Hierarchical Sequential Pattern Mining Evaluation

Anglický název

Generator of Synthetic Datasets for Hierarchical Sequential Pattern Mining Evaluation

Jazyk

en

Originální abstrakt

Evaluation is an important part of algorithm design. Algorithms are typically evaluated on real-world and synthetic datasets. Real-world datasets are appropriate for evaluation of algorithm properties in practice but it is difficult to change the dataset to have some particular statistics, e.g. number of input items. In contrast, generated synthetic dataset simply allows changing any of statistic property of the dataset with keeping all other statistic properties. In the paper, we present a procedure for generation of sequence databases with taxonomies for an evaluation of hierarchical sequential pattern mining algorithms.

Anglický abstrakt

Evaluation is an important part of algorithm design. Algorithms are typically evaluated on real-world and synthetic datasets. Real-world datasets are appropriate for evaluation of algorithm properties in practice but it is difficult to change the dataset to have some particular statistics, e.g. number of input items. In contrast, generated synthetic dataset simply allows changing any of statistic property of the dataset with keeping all other statistic properties. In the paper, we present a procedure for generation of sequence databases with taxonomies for an evaluation of hierarchical sequential pattern mining algorithms.

BibTex


@inproceedings{BUT103555,
  author="Michal {Šebek} and Jaroslav {Zendulka}",
  title="Generator of Synthetic Datasets for Hierarchical Sequential Pattern Mining Evaluation",
  annote="Evaluation is an important part of algorithm design. Algorithms are typically
evaluated on real-world and synthetic datasets. Real-world datasets are
appropriate for evaluation of algorithm properties in practice but it is
difficult to change the dataset to have some particular statistics, e.g. number
of input items. In contrast, generated synthetic dataset simply allows changing
any of statistic property of the dataset with keeping all other statistic
properties. In the paper, we present a procedure for generation of sequence
databases with taxonomies for an evaluation of hierarchical sequential pattern
mining algorithms.",
  address="The University of Technology Košice",
  booktitle="Proceedings of the Twelfth International Conference on Informatics 2013",
  chapter="103555",
  edition="NEUVEDEN",
  howpublished="print",
  institution="The University of Technology Košice",
  year="2013",
  month="november",
  pages="289--292",
  publisher="The University of Technology Košice",
  type="conference paper"
}