An Adaptive and bounded Approach to Mine Frequent Pattern in Large Scale Databases
Main Article Content
Abstract
Frequent Patterns are very important in knowledge discovery and data mining process such as mining of association rules, correlations etc. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to solve association rule mining. In FP-tree isa compact representation of transaction database that contains frequency information of all relevant Frequent Patterns (FP) in a dataset. Mining association rules among items in a large database has been recognized as one of the most important data mining problems. An earlier approach proposes a model that is capable of mining in transactional database, but that approach is not capable of managing the problem of changing the memory dynamically. In order to solve this problem we have been proposed a hybrid of two algorithms that could be able to handle the dynamic change of memory, dynamic databases and also to solve the problem of association rule mining problems. So memory can be utilized effectively in large scale transaction database.
Â
Index terms: Frequent Patterns, transcation database, Apriori algorithm, Association rule, FP tree
Downloads
Article Details
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.