A HYBRID ALGORITHM FOR MINING FREQUENT ITEMSETS IN TRANSACTIONAL DATABASES
Main Article Content
Abstract
Frequent pattern mining is one of the most notable areas under research. Mining frequent itemsets in transactional databases paves way for business improvements. In this paper, a hybrid algorithm called CanTree is proposed, which is based on the classic Apriori and FPGrowth. The proposed algorithm has been derived by improving the existing advantages of both the algorithms and avoiding the recursive generation of conditional pattern bases and sub conditional pattern trees which is the main disadvantage in FPGrowth. The proposed algorithm has been examined by comparing the results with the existing algorithms. The parameters taken for analyzing are time, and memory space. Four different real time datasets with varied sizes from the UCI and Frequent Itemset Mining Implementations Repository (fimi) were used for the experiments. The result shows that the proposed algorithm gives betterment in the mining process of frequent itemsets than the existing algorithms.
Downloads
Download data is not yet available.
Article Details
Section
Articles
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.