Efficient mining regularly frequent patterns in transactional databases

Authors:
Md. Mamunur Rashid;Md. Rezaul Karim;Byeong-Soo Jeong;Ho-Jin Choi
Affiliations:
Dept. of Computer Engineering, Kyung Hee University, Kyunggi-do, Republic of Korea;Dept. of Computer Engineering, Kyung Hee University, Kyunggi-do, Republic of Korea;Dept. of Computer Engineering, Kyung Hee University, Kyunggi-do, Republic of Korea;Computer Science Dept., Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
Venue:
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Year:
2012

Citing 14
Cited 1

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Cyclic Association Rules

ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining Cyclically Repeated Patterns

DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Efficient Mining of Partial Periodic Patterns in Time Series Database

ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure

IEEE Transactions on Knowledge and Data Engineering
Demand-driven frequent itemset mining using pattern structures

Knowledge and Information Systems
Periodicity Detection in Time Series Databases

IEEE Transactions on Knowledge and Data Engineering
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window

Knowledge and Information Systems
Mining maximal hyperclique pattern: A hybrid search strategy

Information Sciences: an International Journal
Efficient single-pass frequent pattern mining using a prefix-tree

Information Sciences: an International Journal
RP-Tree: A Tree Structure to Discover Regular Patterns in Transactional Database

IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Discovering Periodic-Frequent Patterns in Transactional Databases

PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining the k-most interesting frequent patterns sequentially

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning

Mining popular patterns from transactional databases

DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

Finding interesting patterns plays an important role in several data mining applications, such as market basket analysis, medical data analysis, and others. The occurrence frequency of patterns has been regarded as an important criterion for measuring interestingness of a pattern in several applications. However, temporal regularity of patterns can be considered as another important measure for some applications. In this paper, we propose an efficient approach for miming regularly frequent patterns. As for temporal regularity measure, we use variance of interval time between pattern occurrences. To find regularly frequent patterns, we utilize pattern-growth approach according to user given min_support and max_variance threshold. Extensive performance study shows that our approach is time and memory efficient in finding regularly frequent patterns.