On the feasibility of discovering meta-patterns from a data ensemble

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce meta-pattern discovery from a data ensemble, a new paradigm of pattern discovery which goes beyond the KDD process model. A data ensemble, which represents a set of data sets, seems to be more natural as a model of the big data (We focus on the volume and velocity aspects of the big data.). We propose two kinds of meta-patterns, each of which specifies patterns such as clusters for a set of data sets, for an unsupervised setting and a supervised one. Our solutions for these settings were shown to be feasible with one synthetic and two real data ensembles by experiments.

Original languageEnglish
Title of host publicationDiscovery Science - 18th International Conference, DS 2015
EditorsStan Matwin, Nathalie Japkowicz
PublisherSpringer Verlag
Pages266-274
Number of pages9
ISBN (Print)9783319242811
DOIs
Publication statusPublished - Jan 1 2015
Event18th International Conference on Discovery Science, DS 2015 - Banff, Canada
Duration: Oct 4 2015Oct 6 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9356
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Discovery Science, DS 2015
CountryCanada
CityBanff
Period10/4/1510/6/15

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Suzuki, E. (2015). On the feasibility of discovering meta-patterns from a data ensemble. In S. Matwin, & N. Japkowicz (Eds.), Discovery Science - 18th International Conference, DS 2015 (pp. 266-274). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9356). Springer Verlag. https://doi.org/10.1007/978-3-319-24282-8_22