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Authors: Piotr Maciąg and Robert Bembenik

Affiliation: Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

ISBN: 978-989-758-351-3

Keyword(s): Sequential Patterns, Spatio-temporal Data, Crime Incidents.

Abstract: In the paper, we consider the problem of discovering sequential patterns from dataset of event instances and event types. We offer a breadth-first strategy algorithm (spatio-temporal breadth-first miner, STBFM) to search for significant sequential patterns denoting relations between event types in the dataset. We introduce Sequential Pattern Tree (SPTree), a novel structure significantly reducing the time of patterns mining process. Our algorithm is compared with STMiner - the algorithm for discovering sequential patterns from event data. A modification of STBFM allowing to discover Top-N most significant sequential patterns in a given dataset is provided. Experimental studies have been performed on the crime incidents dataset for Boston city.

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Paper citation in several formats:
Maciąg, P. and Bembenik, R. (2019). A Novel Breadth-first Strategy Algorithm for Discovering Sequential Patterns from Spatio-temporal Data.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 459-466. DOI: 10.5220/0007355804590466

@conference{icpram19,
author={Piotr S. Maciąg. and Robert Bembenik.},
title={A Novel Breadth-first Strategy Algorithm for Discovering Sequential Patterns from Spatio-temporal Data},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={459-466},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007355804590466},
isbn={978-989-758-351-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Novel Breadth-first Strategy Algorithm for Discovering Sequential Patterns from Spatio-temporal Data
SN - 978-989-758-351-3
AU - Maciąg, P.
AU - Bembenik, R.
PY - 2019
SP - 459
EP - 466
DO - 10.5220/0007355804590466

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