TY - GEN
T1 - RP-tree
T2 - 9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008
AU - Tanbeer, Syed Khairuzzaman
AU - Ahmed, Chowdhury Farhan
AU - Jeong, Byeong Soo
AU - Lee, Young Koo
N1 - Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - Temporal regularity of pattern appearance can be regarded as an important criterion for measuring the interestingness in several applications like market basket analysis, web administration, gene data analysis, network monitoring, and stock market. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing works is appropriate 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 and propose an efficient data structure, called Regular Pattern tree (RP-tree in short), that enables a pattern growth-based mining technique to generate the complete set of regular patterns in a database for a user-given regularity threshold. Our comprehensive experimental study shows that RP-tree is both time and memory efficient in finding regular pattern.
AB - Temporal regularity of pattern appearance can be regarded as an important criterion for measuring the interestingness in several applications like market basket analysis, web administration, gene data analysis, network monitoring, and stock market. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing works is appropriate 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 and propose an efficient data structure, called Regular Pattern tree (RP-tree in short), that enables a pattern growth-based mining technique to generate the complete set of regular patterns in a database for a user-given regularity threshold. Our comprehensive experimental study shows that RP-tree is both time and memory efficient in finding regular pattern.
KW - Cyclic pattern
KW - Data mining
KW - Pattern mining
KW - Regular pattern
UR - https://www.scopus.com/pages/publications/58049129369
U2 - 10.1007/978-3-540-88906-9_25
DO - 10.1007/978-3-540-88906-9_25
M3 - Conference contribution
AN - SCOPUS:58049129369
SN - 3540889051
SN - 9783540889052
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 193
EP - 200
BT - Intelligent Data Engineering and Automated Learning - IDEAL 2008 - 9th International Conference, Proceedings
PB - Springer Verlag
Y2 - 2 November 2008 through 5 November 2008
ER -