Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Apriori algorithm

An Efficient Association Rules Algorithms for Medical Test Analysis

Ahmed Tariq Sadiq; Alaa Sameer Ali

Engineering and Technology Journal, 2016, Volume 34, Issue 4, Pages 540-546
DOI: 10.30684/etj.34.4B.11

Data Mining denotes mining knowledge from hugequantity of data. All algorithms of association rules mining include ‘first finding frequency of item sets, which accept a minimum support threshold, and then calculates confidence percentage for all k-item sets to construct robust association rules’. The trouble is there are some of algorithms that need more time for compute minimum support, minimum confidence and extraction larger item. In this paper one algorithm is proposed (enhanced reduces items Apriori algorithm) to reduce execution time. The proposed algorithm purpose to introduce algorithm to mine association rules to obtain fast algorithm by reducing execute time. Due to many experiments in (enhanced reduces items Apriori algorithm), this algorithm is very fast compared with (topk-rules and topk-non redundant rules) algorithms.

An Improved Distributed Association Rule Algorithm

Saad K. Majeed; Hussein K. Abbas

Engineering and Technology Journal, 2010, Volume 28, Issue 18, Pages 5695-5710

All Distributed association rules mining (DARM) algorithms which bases on Apriori algorithm don't have an efficient message optimization technique, so they exchange numerous messages during the mining process which needs several distributed scan operations to the distributed warehouses or distributed databases to get the support values, also the performance of these DARM algorithms decreased with increasing communication cost especially when increasing the number of
distributed mining sites as well as the itemsets to be mined become more larger . The aim of this work is to improve association rules in distributed data mining by proposing a new efficient method of distributed association rule mining, which reduce the average size of records transferred, datasets and messages transferred without need
to any distributed scan to the distributed data warehouses or distributed databases to retrieve the values of the support values of these datasets. The results obtained from the proposed method prove that the proposed method is better than the existing algorithms by reducing communications costs, centralstorage requirements, enhance
performance and achieves high degree of scalability compared with the existing algorithms.