Finding Association Rules Based on Maximal Frequent Itemsets over Data Streams Adaptively
Main Article Content
Abstract
Overflow of data streams are gathered and manipulated in sensor networks, communication networks, Internet traffic, and online transaction in financial market, power grids, and industry production processes, scientific and engineering experiments to yield better analysis. In contrast to conventional data sets, stream data have infiltrated from systems temporally ordered, rapidly fluctuated, massive and potentially infinite. It would be potentially cumbersome and very exponential to store the entire data streams or scan through it multiple times due to its tremendous volume.
This paper proposes the strategies to mine maximal data items and its data itemsets in single scan. Besides it generates association rules based on top maximal itemsets and data itemsets, which contain current and useful information for effective data analysis.
Keywords: Data Streams, Association Rule Mining, Memory Utilization, Frequent Itemsets, Hash Table.
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.