Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses

Marcin Gorawski; Pawel Jureczek

doi:10.1007/978-3-642-13769-3_23

Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses

Marcin Gorawski
, Pawel Jureczek

Katedra Informatyki Stosowanej (RAU/RAU7)

Silesian University of Technology

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

7 Cytowania z bazy Scopus

Abstrakt

This paper presents the FCP-Tree index structure and the new algorithm for continuous pattern mining, called FCPGrowth, for Trajectory Data Warehouses. The FCP-Tree is an aggregate tree which allows storing similar sequences in the same nodes. A characteristic feature of the FCPGrowth algorithm is that it does not require constructing intermediate trees at recursion levels and therefore, it has small memory requirements. In addition, when the initial FCP-Tree is built, input sequences are split on infrequent elements, thereby increasing the compactness of this structure. The FCPGrowth algorithm is much more efficient than our previous algorithm, which is confirmed experimentally in this paper.

Język oryginału	angielski
Tytuł publikacji goszczącej	Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings
Strony	187-195
Liczba stron	9
Edycja	PART 1
Identyfikatory DOI	https://doi.org/10.1007/978-3-642-13769-3_23
Status publikacji	Opublikowano - 2010
Wydarzenie	5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010 - San Sebastian, Hiszpania Czas trwania: 23 cze 2010 → 25 cze 2010

Seria publikacji

Nazwa	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Numer	PART 1
Tom	6076 LNAI
ISSN (drukowany)	0302-9743
ISSN (elektroniczny)	1611-3349

Konferencja

Konferencja	5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010
Kraj/Terytorium	Hiszpania
Miejscowość	San Sebastian
Okres	23/06/10 → 25/06/10

Obszary tematyczne ASJC Scopus

Informatyka teoretyczna
Informatyka ogólna

Uzyskaj dostęp do dokumentu

10.1007/978-3-642-13769-3_23

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Link do publikacji w Scopus

Cytowanie

Gorawski, M., & Jureczek, P. (2010). Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses. W Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings (PART 1 red., str. 187-195). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Tom 6076 LNAI, Nr PART 1). https://doi.org/10.1007/978-3-642-13769-3_23

Gorawski, Marcin ; Jureczek, Pawel. / Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses. Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings. PART 1. red. 2010. str. 187-195 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{ebee087c5261441bafc5aae3dcf5efc6,

title = "Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses",

abstract = "This paper presents the FCP-Tree index structure and the new algorithm for continuous pattern mining, called FCPGrowth, for Trajectory Data Warehouses. The FCP-Tree is an aggregate tree which allows storing similar sequences in the same nodes. A characteristic feature of the FCPGrowth algorithm is that it does not require constructing intermediate trees at recursion levels and therefore, it has small memory requirements. In addition, when the initial FCP-Tree is built, input sequences are split on infrequent elements, thereby increasing the compactness of this structure. The FCPGrowth algorithm is much more efficient than our previous algorithm, which is confirmed experimentally in this paper.",

author = "Marcin Gorawski and Pawel Jureczek",

year = "2010",

doi = "10.1007/978-3-642-13769-3\_23",

language = "English",

isbn = "3642137687",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "187--195",

booktitle = "Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings",

edition = "PART 1",

note = "5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010 ; Conference date: 23-06-2010 Through 25-06-2010",

}

Gorawski, M & Jureczek, P 2010, Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses. w Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings. PART 1 wyd., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), nr PART 1, tom 6076 LNAI, str. 187-195, 5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010, San Sebastian, Hiszpania, 23/06/10. https://doi.org/10.1007/978-3-642-13769-3_23

Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses. / Gorawski, Marcin; Jureczek, Pawel.
Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings. PART 1. red. 2010. str. 187-195 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Tom 6076 LNAI, Nr PART 1).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

TY - GEN

T1 - Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses

AU - Gorawski, Marcin

AU - Jureczek, Pawel

PY - 2010

Y1 - 2010

N2 - This paper presents the FCP-Tree index structure and the new algorithm for continuous pattern mining, called FCPGrowth, for Trajectory Data Warehouses. The FCP-Tree is an aggregate tree which allows storing similar sequences in the same nodes. A characteristic feature of the FCPGrowth algorithm is that it does not require constructing intermediate trees at recursion levels and therefore, it has small memory requirements. In addition, when the initial FCP-Tree is built, input sequences are split on infrequent elements, thereby increasing the compactness of this structure. The FCPGrowth algorithm is much more efficient than our previous algorithm, which is confirmed experimentally in this paper.

AB - This paper presents the FCP-Tree index structure and the new algorithm for continuous pattern mining, called FCPGrowth, for Trajectory Data Warehouses. The FCP-Tree is an aggregate tree which allows storing similar sequences in the same nodes. A characteristic feature of the FCPGrowth algorithm is that it does not require constructing intermediate trees at recursion levels and therefore, it has small memory requirements. In addition, when the initial FCP-Tree is built, input sequences are split on infrequent elements, thereby increasing the compactness of this structure. The FCPGrowth algorithm is much more efficient than our previous algorithm, which is confirmed experimentally in this paper.

UR - https://www.scopus.com/pages/publications/77954607723

U2 - 10.1007/978-3-642-13769-3_23

DO - 10.1007/978-3-642-13769-3_23

M3 - Conference contribution

AN - SCOPUS:77954607723

SN - 3642137687

SN - 9783642137686

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 187

EP - 195

BT - Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings

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ER -

Gorawski M, Jureczek P. Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses. W Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings. PART 1 red. 2010. str. 187-195. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-13769-3_23

Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses

Abstrakt

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Konferencja

Obszary tematyczne ASJC Scopus

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