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Application of Incremental Mining and Apriori Algorithm on Library Transactional Database
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 73 - Number 8 |
| Year of Publication: 2013 |
| Authors: Gunjan Mehta, Deepa Sharma, Ekta Chauhan |
10.5120/12760-9336
|
Gunjan Mehta, Deepa Sharma, Ekta Chauhan . Application of Incremental Mining and Apriori Algorithm on Library Transactional Database. International Journal of Computer Applications. 73, 8 ( July 2013), 12-18. DOI=10.5120/12760-9336
Abstract
Data mining is used to extract hidden, predictive information from large databases, which can be used for predicting future trends and allowing businesses to make knowledge- driven decisions [1]. In this paper we explain how Apriori algorithm can be applied on the university's library transactional database in order to find out the frequent book items and generate rules on these book items so as to predict the book borrowing behavior of the students. It then explains how incremental mining when incorporated by adding five more transactions to the original set of ten transactions changes the number of frequent item-sets and association rules generated by the algorithm.
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