Toward mining of spatiotemporal maximal frequent patterns

POPELÍNSKÝ, Lubomír and Jan BLAŤÁK. Toward mining of spatiotemporal maximal frequent patterns. In Proceedings of ECML/PKDD Workshop on Mining Spatio-Temporal Data (MSTD),. Porto: UP, 2005, p. 31-40.

Basic information

Original name

Toward mining of spatiotemporal maximal frequent patterns

Name in Czech

K dolování v prostorově-časových datech

Authors

POPELÍNSKÝ, Lubomír (203 Czech Republic, guarantor) and Jan BLAŤÁK (203 Czech Republic)

Edition

Porto, Proceedings of ECML/PKDD Workshop on Mining Spatio-Temporal Data (MSTD), p. 31-40, 10 pp. 2005

Publisher

UP

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Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Portugal

Confidentiality degree

není předmětem státního či obchodního tajemství

RIV identification code

RIV/00216224:14330/05:00014243

Organization unit

Faculty of Informatics

Keywords in English

data mining; spatiotemporal data

Tags

data mining, spatiotemporal data

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Abstract

V originále

We show that propositional spatiotemporal logic PSTL is a powerful tool for mining in various spatiotemporal data including environmental and medical data, keystroke dynamics data or text. We introduce a refinement operator for a fragment of $PSTL$, $ST_0$ and %, and present frequent patterns mined with RAP. describe the ILP system GRAPE for mining first-order frequent patterns in spatiotemporal data. We also show that in the classification task %the use of this refinement operator can %decrease computational cost and that the use of frequent patterns as new features result in an accuracy increase.

In Czech

We show that propositional spatiotemporal logic PSTL is a powerful tool for mining in various spatiotemporal data including environmental and medical data, keystroke dynamics data or text. We introduce a refinement operator for a fragment of $PSTL$, $ST_0$ and %, and present frequent patterns mined with RAP. describe the ILP system GRAPE for mining first-order frequent patterns in spatiotemporal data. We also show that in the classification taskthe use of frequent patterns as new features result in an accuracy increase.

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Links

MSM0021622418, plan (intention)

Name: DYNAMICKÁ GEOVIZUALIZACE V KRIZOVÉM MANAGEMENTU Investor: Ministry of Education, Youth and Sports of the CR, Dynamic Geovisualisation in Crises Management