The pattern next door: Towards spatio-sequential pattern discovery

Hugo Alatrista Salas; Sandra Bringay; Frédéric Flouvat; Nazha Selmaoui-Folcher; Maguelonne Teisseire

doi:10.1007/978-3-642-30220-6_14

The pattern next door: Towards spatio-sequential pattern discovery

Hugo Alatrista Salas
, Sandra Bringay
, Frédéric Flouvat
, Nazha Selmaoui-Folcher
, Maguelonne Teisseire

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Scopus citations

Abstract

Health risks management such as epidemics study produces large quantity of spatio-temporal data. The development of new methods able to manage such specific characteristics becomes crucial. To tackle this problem, we define a theoretical framework for extracting spatio-temporal patterns (sequences representing evolution of locations and their neighborhoods over time). Classical frequency support doesn't consider the pattern neighbor neither its evolution over time. We thus propose a new interestingness measure taking into account both spatial and temporal aspects. An algorithm based on pattern-growth approach with efficient successive projections over the database is proposed. Experiments conducted on real datasets highlight the relevance of our method.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
Pages	157-168
Number of pages	12
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-30220-6_14
State	Published - 2012
Externally published	Yes
Event	16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012 - Kuala Lumpur, Malaysia Duration: 29 May 2012 → 1 Jun 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7301 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
Country/Territory	Malaysia
City	Kuala Lumpur
Period	29/05/12 → 1/06/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-642-30220-6_14

Cite this

Alatrista Salas, H., Bringay, S., Flouvat, F., Selmaoui-Folcher, N., & Teisseire, M. (2012). The pattern next door: Towards spatio-sequential pattern discovery. In Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings (PART 2 ed., pp. 157-168). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7301 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-30220-6_14

Alatrista Salas, Hugo ; Bringay, Sandra ; Flouvat, Frédéric et al. / The pattern next door : Towards spatio-sequential pattern discovery. Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings. PART 2. ed. 2012. pp. 157-168 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "The pattern next door: Towards spatio-sequential pattern discovery",

abstract = "Health risks management such as epidemics study produces large quantity of spatio-temporal data. The development of new methods able to manage such specific characteristics becomes crucial. To tackle this problem, we define a theoretical framework for extracting spatio-temporal patterns (sequences representing evolution of locations and their neighborhoods over time). Classical frequency support doesn't consider the pattern neighbor neither its evolution over time. We thus propose a new interestingness measure taking into account both spatial and temporal aspects. An algorithm based on pattern-growth approach with efficient successive projections over the database is proposed. Experiments conducted on real datasets highlight the relevance of our method.",

author = "\{Alatrista Salas\}, Hugo and Sandra Bringay and Fr{\'e}d{\'e}ric Flouvat and Nazha Selmaoui-Folcher and Maguelonne Teisseire",

year = "2012",

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Alatrista Salas, H, Bringay, S, Flouvat, F, Selmaoui-Folcher, N & Teisseire, M 2012, The pattern next door: Towards spatio-sequential pattern discovery. in Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7301 LNAI, pp. 157-168, 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012, Kuala Lumpur, Malaysia, 29/05/12. https://doi.org/10.1007/978-3-642-30220-6_14

The pattern next door: Towards spatio-sequential pattern discovery. / Alatrista Salas, Hugo; Bringay, Sandra; Flouvat, Frédéric et al.
Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings. PART 2. ed. 2012. p. 157-168 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7301 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - The pattern next door

T2 - 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012

AU - Alatrista Salas, Hugo

AU - Bringay, Sandra

AU - Flouvat, Frédéric

AU - Selmaoui-Folcher, Nazha

AU - Teisseire, Maguelonne

PY - 2012

Y1 - 2012

N2 - Health risks management such as epidemics study produces large quantity of spatio-temporal data. The development of new methods able to manage such specific characteristics becomes crucial. To tackle this problem, we define a theoretical framework for extracting spatio-temporal patterns (sequences representing evolution of locations and their neighborhoods over time). Classical frequency support doesn't consider the pattern neighbor neither its evolution over time. We thus propose a new interestingness measure taking into account both spatial and temporal aspects. An algorithm based on pattern-growth approach with efficient successive projections over the database is proposed. Experiments conducted on real datasets highlight the relevance of our method.

AB - Health risks management such as epidemics study produces large quantity of spatio-temporal data. The development of new methods able to manage such specific characteristics becomes crucial. To tackle this problem, we define a theoretical framework for extracting spatio-temporal patterns (sequences representing evolution of locations and their neighborhoods over time). Classical frequency support doesn't consider the pattern neighbor neither its evolution over time. We thus propose a new interestingness measure taking into account both spatial and temporal aspects. An algorithm based on pattern-growth approach with efficient successive projections over the database is proposed. Experiments conducted on real datasets highlight the relevance of our method.

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Alatrista Salas H, Bringay S, Flouvat F, Selmaoui-Folcher N, Teisseire M. The pattern next door: Towards spatio-sequential pattern discovery. In Advances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings. PART 2 ed. 2012. p. 157-168. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-30220-6_14

The pattern next door: Towards spatio-sequential pattern discovery

Abstract

Publication series

Conference

UN SDGs

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