An efficient association rules algorithms for medical test analysis

Abstract EN

Data Mining denotes mining knowledge from huge quantity 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 (to pk-rules and to pk-non redundant rules) algorithms.