Dynamic Association Rule Mining using Genetic Algorithms

Authors:
P. Deepa Shenoy;K. G. Srinivasa;K. R. Venugopal;Lalit M. Patnaik
Affiliations:
Dept. of Elec. Eng., Univ. Visvesvaraya Coll. of Eng., Bangalore Univ., K R Circle, Bangalore 560001, India. Tel.: +91 80 26671536/ Fax: +91 80 22276070/ E-mail: shenoypd@yahoo.com/ kgsrinivas@msr ...;Dept. of Elec. Eng., Univ. Visvesvaraya Coll. of Eng., Bangalore Univ., K R Circle, Bangalore 560001, India. Tel.: +91 80 26671536/ Fax: +91 80 22276070/ E-mail: shenoypd@yahoo.com/ kgsrinivas@msr ...;Dept. of Elec. Eng., Univ. Visvesvaraya Coll. of Eng., Bangalore Univ., K R Circle, Bangalore 560001, India. Tel.: +91 80 26671536/ Fax: +91 80 22276070/ E-mail: shenoypd@yahoo.com/ kgsrinivas@msr ...;Microprocessor Applications Laboratory, Department of CSA, Indian Institute of Science, Bangalore 560012, India. E-mail: lalit@micro.iisc.ernet.in
Venue:
Intelligent Data Analysis
Year:
2005

Citing 13
Cited 3

Genetic algorithms: A Survey

Computer
An effective hash-based algorithm for mining association rules

SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Scalable parallel data mining for association rules

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Parallel mining algorithms for generalized association rules with classification hierarchy

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Breaking the barrier of transactions: mining inter-transaction association rules

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond intratransaction association analysis: mining multidimensional intertransaction association rules

ACM Transactions on Information Systems (TOIS)
A fast distributed algorithm for mining association rules

DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Parallel Mining of Association Rules

IEEE Transactions on Knowledge and Data Engineering
DEMON: Mining and Monitoring Evolving Data

IEEE Transactions on Knowledge and Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique

ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A General Incremental Technique for Maintaining Discovered Association Rules

Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Evolutionary approach for mining association rules on dynamic databases

PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining

Lesion detection using segmentation and classification of mammograms

AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Mining dynamic association rules with comments

Knowledge and Information Systems
Evolutionary and immune algorithms applied to association rule mining

SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A large volume of transaction data is generated everyday in a number of applications. These dynamic data sets have immense potential for reflecting changes in customer behaviour patterns. One of the strategies of data mining is association rule discovery which correlates the occurrence of certain attributes in the database leading to the identification of large data itemsets. This paper seeks to generate large itemsets in a dynamic transaction database using the principles of Genetic Algorithms. Intra Transactions, Inter Transactions and Distributed Transactions are considered for mining Association Rules. Further, we analyze the time complexities of single scan technique DMARG (Dynamic Mining of Association Rules using Genetic Algorithms), with Fast UPdate (FUP) algorithm for intra transactions and E-Apriori for inter transactions. Our study shows that the algorithm DMARG outperforms both FUP and E-Apriori in terms of execution time and scalability, without compromising the quality or completeness of rules generated.