doi: 10.7763/IJCTE.2012.V4.620
Data Partitioning and Bit Vector Approach for Weighted Frequent Item Set Mining
M. A. Ja bbar1
, ,
- 1JNTU Hyderabad, India.
- 2Visiting Professor HCU, Hyderabad, India.
- 3Scientist Advanced systems Laboratory India.
Abstract
Association rule mining is an important task in data mining. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement and inventory control. Most of the association rule mining algorithms will not consider the weight of an item. Weighted association is very important in KDD.In analyzing market basket analysis, people often use apriori algorithm, but apriori generates large number of frequent item sets. One alternate approach to apriori is partitioning technique. This paper presents a method to find weighted frequent item sets using partitioning and bit vector .By example, it is proved that partitioning technique can improve the efficiency by reducing the number of candidates.
Keywords
- Weighted association rules
- KDD
- partitioning
- bit vector
- weighted support
How to Cite
M. A. Ja bbar, B. L. D eekshatulu, and Priti Chandra, "Data Partitioning and Bit Vector Approach for Weighted Frequent Item Set Mining," International Journal of Computer Theory and Engineering, vol. 4, no. 6, pp. 980-982, 2012. https://doi.org/10.7763/IJCTE.2012.V4.620
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).