An Efficient Algorithm for Mining Frequent Itemsets in Large Databases

Authors

B. Praveen Kumar, Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Tamilnadu, IndiaFollow
D. Paulraj, Department of Computer Science and Engineering, R.M.D. Engineering College, Tamilnadu, IndiaFollow

Author Country (or Countries)

India

Abstract

Flexible Bitmap Index and Cluster based Bit Vector (FBICBV), focus on identifying the eligible candidates from unfiltered huge volume of temporal data in order to find out frequent patterns among them. One of the best and efficient solutions is to use the mechanism of bitmap indexing and clustering. First, using bitmap index, the rich data are filtered out from the unfiltered raw dataset to be analyzed effectively. Thus, the eligible candidate data are identified through this process. Second, the frequent patterns are identified using cluster based bit vector appropriate for effective decision making. Hence, scanning of raw data is completely avoided using bitmap index. Also, it eliminates the storage of the candidate’s unnecessary data while forming a cluster table. Consequently, it implies improvements in optimizing the database storage for maximum performance and efficiency using FBICBV algorithm compared existing algorithms.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/130604

Recommended Citation

Praveen Kumar, B. and Paulraj, D. (2019) "An Efficient Algorithm for Mining Frequent Itemsets in Large Databases," Applied Mathematics & Information Sciences: Vol. 13: Iss. 6, Article 4.
DOI: http://dx.doi.org/10.18576/amis/130604
Available at: https://digitalcommons.aaru.edu.jo/amis/vol13/iss6/4