Applying correlation threshold on Apriori algorithm

Ever growing size of information and database has always demanded the scientific world for very efficient rule mining algorithm. This paper gives an extension to the Apriori algorithm, a classical rule mining algorithm. Apriori finds its application in areas of data mining, finding association between attributes and in prediction systems. Even though Apriori suits in various applications it possesses various disadvantages. To increase the efficiency of the present Apriori algorithm a method for incorporating a new correlation factor (threshold) is being introduced. First part of the paper provides a quick summary of basic Apriori algorithm and second half details the implementation of correlation threshold. Performance of the redesigned algorithm is evaluated and is compared with the traditional Apriori algorithm. The evaluation shows a peak improvement in the mining result. We reduce the time complexity of the newly designed algorithm into O (n). In an application level, qualitative content analysis of water was also conducted to affirm the results.