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

Department of Applied Informatics (RAU/RAU7)

Silesian University of Technology

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

7 Citations (Scopus)

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.

Original language	English
Title of host publication	Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings
Pages	187-195
Number of pages	9
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-13769-3_23
Publication status	Published - 2010
Event	5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010 - San Sebastian, Spain Duration: 23 Jun 2010 → 25 Jun 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6076 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010
Country/Territory	Spain
City	San Sebastian
Period	23/06/10 → 25/06/10

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

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

Cite this

Gorawski, M., & Jureczek, P. (2010). Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses. In Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings (PART 1 ed., pp. 187-195). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6076 LNAI, No. 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. ed. 2010. pp. 187-195 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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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",

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Gorawski, M & Jureczek, P 2010, Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses. in Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6076 LNAI, pp. 187-195, 5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010, San Sebastian, Spain, 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. ed. 2010. p. 187-195 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6076 LNAI, No. PART 1).

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

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PY - 2010

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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.

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

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

M3 - Conference contribution

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SN - 9783642137686

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Gorawski M, Jureczek P. Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses. In Hybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings. PART 1 ed. 2010. p. 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

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