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An Efficient Association Rule Mining by Optimal Multiple-Core Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 146 - Number 2 |
| Year of Publication: 2016 |
| Authors: Aasma Parveen, Shrikant Tiwari |
10.5120/ijca2016910678
|
Aasma Parveen, Shrikant Tiwari . An Efficient Association Rule Mining by Optimal Multiple-Core Algorithm. International Journal of Computer Applications. 146, 2 ( Jul 2016), 16-20. DOI=10.5120/ijca2016910678
Abstract
Association mining aims to extract frequent patterns, interesting correlations, associations or casual structures among the sets of objects in the transaction files or from the other data repositories. It plays a vital role in spawning frequent item sets from large transaction databases. The discovery of interesting association relationship among business transaction records in many commercial decision making method such as catalog decision, cross-marketing, and loss-leader analysis. It is also used to excerpt hidden information from large datasets. The Association Rule Mining algorithms such as Apriori, FP-Growth wants repetitive scans over the entire file. All the input/output overheads that are being generated during the frequent perusing process, entire file decreases the performance of CPU, memory and I/O overheads. In this paper we have proposed An Cohesive tactic of Parallel Processing and ARM for mining Association Rules on Generalized data set that is basically altered from all the previous algorithms in that it uses database in transposed form and database rearrangement is done using Parallel rearrangement algorithm (Shuffle Transpose) so to generate all important association rules number of passes essential is abridged. Equaled various classical Association Rule Mining algorithms and topical procedures.
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