Incremental Frequent Pattern Mining
Jyoti Jadhav1, Lata Ragha2, Vijay Katkar3
1Miss. Jyoti Jadhav, Computer Department, Mumbai/ R.A.I.T/ DY. Patil, Navi Mumbai, India.
2Dr. Lata Ragha, Computer Department, Mumbai/ Terna Engineering College, Navi Mumbai, India.
3Mr. Vijay Katkar, His Department Name, Pune/ PCCOE, Pune, India.
Manuscript received on July 17, 2012. | Revised Manuscript received on August 25, 2012. | Manuscript published on August 30, 2012. | PP: 223-228 | Volume-1 Issue-6, August 2012. | Retrieval Number: F0672081612/2012©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Association rule discovery is widely used Data Mining technique for Market Basket Analysis. It discovers interesting correlations and frequent patterns from the database. In real life, new transactions are continuously added to the database as time advances. This result in; periodic change in correlations and frequent patterns present in database. Incremental Association Rule mining is used to handle this situation. Most of the existing Incremental rule mining methods are highly dependent on availability of main memory. If sufficient amount of main memory is not available, they fail to generate the results. This paper presents a novel method for incremental discovery of frequent patterns using Main Memory database Management System to eliminate this drawback. Experimental results are provided to support the efficiency of proposed method.
Keywords: Apriori, FP-tree, Incremental Association Rule Mining, Main memory database Management System.