Optimization of Active Apriori Algorithm Using an Effective Genetic Algorithm for the Identification of Top-l Elements in a Peer to Peer Network


  • S. Veena Sathyabama university
  • P. Rangarajan


Data mining, Active Apriori algorithm, Effective Genetic algorithm.


In a distributed system like peer to peer network, there are two ways of storing the data namely homogeneous and heterogeneous. Mining the homogeneous data  in a client is less time consuming and fast compared to the mining in the server. Frequent sets play an essential role in many Data Mining tasks that try to find interesting patterns from databases, such as association rules, correlations, sequences, episodes, classifiers and clusters. The mining of association rules is one of the most  popular problems of all these. In this paper, Active Apriori algorithm is used to find the frequent items in the data set which reduces the cost. This method compresses the database by removing unnecessary transaction records and data items from the database that are not used for further processing. The speed of algorithm is increased because it needs to scan only the compressed database and not entire database. The results of the Active apriori algorithm can be optimized using an effective genetic algorithm to identify the top l elements or most frequent item sets. In this method,  the near distance of rule set are found using equalize distance formula and generate two classes namely, higher class and lower class . The classes are validated by distance weight vector, which  maintains a threshold value of rule item set. This Effective genetic algorithm is mainly used for optimization of rule set.


Kanishka Bhaduri, Kamalika Das, Kun Liu, Hillol Kargupta, “Distributed Identification of Top-l Inner Product Elements and its Application in a Peer-to-Peer Networkâ€, CIKM , 2006.

K. Liu, H. Kargupta, and J. Ryan, “Random Projection-Based Multiplicative Data Perturbation for Privacy Preserving Distributed Data Mining,†IEEE Trans. Knowledge and Data Eng., vol. 18, no. 1, pp. 92-106, Jan. 2006.

Souptik Datta, Hillol Kargupta, “A communication efficient probabilistic algorithm for mining frequent itemsets from a peer-to-peer network,†Article first published online: 16 JUN 2009.

Yiwu Xie, Yutong Li, Chunli Wang, Mingyu Lu â€The Optimization and Improvement of the Apriori Algorithmâ€, Intelligent Information Technology Application Workshops, 2008. IITAW '08.

Gao, Shao-jun Li,’ A method of improvement and optimization on association rules appriori algorithm’,proceeding of the 6th congress on intelligent control and automation,2006 pp5901-5905.

By Rakesh Agrawal Ramakrishnan Srikant_ Fast Algorithms for Mining Association RulesVLDB ConferenceSantiago, Chile, 1994.

R. Agrawal and R. Srikant. “Fast algorithms for mining association rules in large databases†Research Report RJ 9839, IBM Almaden Research Center, San Jose, California, June 1994.

By N. Chaiyarataiia and A. M. S. Zalzala Recent Developments in Evolutionary and Genetic Algorithms: Theory and Applications Innovations and Applications, 2-4 September 1997, Conference Publication NO. 4 4 6 ,IEEE , 1997.

By Pengfei Guo Xuezhi Wang Yingshi Han The Enhanced Genetic Algorithms for the Optimization Design 978-1-4244-6498-2/10/$26.00 © IEEE 2010.

By Dieferson Luis Alves de Araujo’ , Heitor S. Lopes’, Alex A. Freitas2 A Parallel Genetic Algorithm for Rule Discovery in Large Databases 0-7803-5731-0/99$$10.00109 99 IEEE.

By Xiaofeng Yuan, Hualong Xu, and Shuhong Chen “Improvement on the Constrained Association Rule Mining Algorithm of Separate†1-4244-0682-X/06/$20.00 © IEEE 2006.

By WEI Yong-qing1, YANG Ren-hua2, LIU Pei-yu2 An Improved Apriori Algorithm for Association Rules of Mining 978-1-4244-3930-0/09/$25.00 © IEEE 2009.

By R. Uday Kiran and P. Krishna Reddy An Improved Multiple Minimum Support Based Approach to Mine Rare Association Rules 978-1-4244-2765-9/09/$25.00 © IEEE 2009.

By Peter P. Wakabi ,Waiswa ,Venansius Baryamureeba, Karunakaran and Sarukesi “Optimized Association Rule Mining with Genetic Algorithms†Natural Computation 978-1-4244-9953-3/11/$26.00 © IEEE 2011.

By Li-Min Tsai, Shu-Jing Lin, and Don-Lin Yang “Efficient Mining of Generalized Negative Association Rules†Granular Computing 978-0-7695-4161-7/10 $26.00 © IEEE 2010.

Rajneesh K Karan, YK Rana.By Rakesh Agrawal Tomasz Imielinski Arun Swami Mining Association Rules between Sets of Items in Large Databases ACM SIGMOD Conference Washington DC, USA, May 1993.

Badri Patel, Vijay K Chaudhari, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-1, Issue-1, March 2011Optimization of Association Rule Mining Apriori Algorithm Using ACO

“Association rule mining over multiple databases: Partitioned and incremental approaches†by Hima Valli Kona, the University of Texas at Arlington, December 2003.




How to Cite

Veena, S., & Rangarajan, P. (2014). Optimization of Active Apriori Algorithm Using an Effective Genetic Algorithm for the Identification of Top-l Elements in a Peer to Peer Network. Asian Journal of Applied Sciences, 2(5). Retrieved from https://www.ajouronline.com/index.php/AJAS/article/view/1765