Shenoy, Deepa P and Srinivasa, KG and Venugopal, KR and Patnaik, LM (2003) Evolutionary approach for mining association rules on dynamic databases. In: 7th Pacific-Asia Conference, April 30 – May 2, 2003, Seoul, Korea.
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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 axe considered for mining association rules. Further, we analyze the time complexities of single scan DMARG (Dynamic Mining of Association Rules using Genetic Algorithms), with Fast UP date (FUP) algorithm for intra transactions and E-Apriori for inter transactions. Our study shows that DMARG outperforms both FUP and E-Apriori in terms of execution time and scalability, without compromising the quality or completeness of rules generated. The problem of mining association rules in the distributed environment is explored in DDMARG (Distributed and Dynamic Mining of Association Rules using Genetic Algorithms).
|Item Type:||Conference Paper|
|Additional Information:||Copyright of this article belongs to Springer.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||11 May 2009 08:24|
|Last Modified:||19 Sep 2010 04:54|
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