Mining Regular Patterns in Transactional Databases

Syed Khairuzzaman TANBEER; Chowdhury Farhan AHMED; Byeong-Soo JEONG; Young-Koo LEE

doi:10.1093/ietisy/e91-d.11.2568

Mining Regular Patterns in Transactional Databases

Syed Khairuzzaman TANBEER, Chowdhury Farhan AHMED, Byeong-Soo JEONG, Young-Koo LEE

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The frequency of a pattern may not be a sufficient criterion for identifying meaningful patterns in a database. The temporal regularity of a pattern can be another key criterion for assessing the importance of a pattern in several applications. A pattern can be said regular if it appears at a regular user-defined interval in the database. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing studies have provided an appropriate method for discovering the patterns that occur regularly in a transactional database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from transactional databases. We also devise an efficient tree-based data structure, called a Regular Pattern tree (RP-tree in short), that captures the database contents in a highly compact manner and enables a pattern growth-based mining technique to generate the complete set of regular patterns in a database for a user-defined regularity threshold. Our performance study shows that mining regular patterns with an RP-tree is time and memory efficient, as well as highly scalable.

Publication: IEICE TRANSACTIONS on Information Vol.E91-D No.11 pp.2568-2577

Publication Date: 2008/11/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e91-d.11.2568

Type of Manuscript: Special Section PAPER (Special Section on Knowledge, Information and Creativity Support System)

Category: Knowledge Discovery and Data Mining

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Syed Khairuzzaman TANBEER, Chowdhury Farhan AHMED, Byeong-Soo JEONG, Young-Koo LEE, "Mining Regular Patterns in Transactional Databases" in IEICE TRANSACTIONS on Information, vol. E91-D, no. 11, pp. 2568-2577, November 2008, doi: 10.1093/ietisy/e91-d.11.2568.
Abstract: The frequency of a pattern may not be a sufficient criterion for identifying meaningful patterns in a database. The temporal regularity of a pattern can be another key criterion for assessing the importance of a pattern in several applications. A pattern can be said regular if it appears at a regular user-defined interval in the database. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing studies have provided an appropriate method for discovering the patterns that occur regularly in a transactional database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from transactional databases. We also devise an efficient tree-based data structure, called a Regular Pattern tree (RP-tree in short), that captures the database contents in a highly compact manner and enables a pattern growth-based mining technique to generate the complete set of regular patterns in a database for a user-defined regularity threshold. Our performance study shows that mining regular patterns with an RP-tree is time and memory efficient, as well as highly scalable.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.11.2568/_p

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@ARTICLE{e91-d_11_2568,
author={Syed Khairuzzaman TANBEER, Chowdhury Farhan AHMED, Byeong-Soo JEONG, Young-Koo LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Mining Regular Patterns in Transactional Databases},
year={2008},
volume={E91-D},
number={11},
pages={2568-2577},
abstract={The frequency of a pattern may not be a sufficient criterion for identifying meaningful patterns in a database. The temporal regularity of a pattern can be another key criterion for assessing the importance of a pattern in several applications. A pattern can be said regular if it appears at a regular user-defined interval in the database. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing studies have provided an appropriate method for discovering the patterns that occur regularly in a transactional database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from transactional databases. We also devise an efficient tree-based data structure, called a Regular Pattern tree (RP-tree in short), that captures the database contents in a highly compact manner and enables a pattern growth-based mining technique to generate the complete set of regular patterns in a database for a user-defined regularity threshold. Our performance study shows that mining regular patterns with an RP-tree is time and memory efficient, as well as highly scalable.},
keywords={},
doi={10.1093/ietisy/e91-d.11.2568},
ISSN={1745-1361},
month={November},}

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TY - JOUR
TI - Mining Regular Patterns in Transactional Databases
T2 - IEICE TRANSACTIONS on Information
SP - 2568
EP - 2577
AU - Syed Khairuzzaman TANBEER
AU - Chowdhury Farhan AHMED
AU - Byeong-Soo JEONG
AU - Young-Koo LEE
PY - 2008
DO - 10.1093/ietisy/e91-d.11.2568
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2008
AB - The frequency of a pattern may not be a sufficient criterion for identifying meaningful patterns in a database. The temporal regularity of a pattern can be another key criterion for assessing the importance of a pattern in several applications. A pattern can be said regular if it appears at a regular user-defined interval in the database. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing studies have provided an appropriate method for discovering the patterns that occur regularly in a transactional database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from transactional databases. We also devise an efficient tree-based data structure, called a Regular Pattern tree (RP-tree in short), that captures the database contents in a highly compact manner and enables a pattern growth-based mining technique to generate the complete set of regular patterns in a database for a user-defined regularity threshold. Our performance study shows that mining regular patterns with an RP-tree is time and memory efficient, as well as highly scalable.
ER -