Inter — Transactional pattern discovery applying comparative apriori and modified reverse apriori approach

Abstract

In this paper, a pattern trend-based data mining approach has been proposed which convert the numeric stock data to symbolic notations, carries out association analysis through comparative study of apriori and proposed modified reverse apriori concepts and further applies the mined rules in predicting the movement of prices. Application of modified reverse apriori has shown drastic reduction in the number of scans. The apriori covers 105scans in performing the evaluation whereas the applied modified reverse apriori covers the same in just 28 scans which is a surprising result. The initial formulation is based on inter-stock mining. The execution time is also evaluated and observed that modified reverse apriori takes less execution time as compared to apriori. There is a roughly 5221 milliseconds (approx) of difference between the both. A comparative study is shown along with the discovery of important pattern trends which shows the investing benefits for the clients in the stock market. This provides a very significant way of evaluating the position of the stocks i.e the highest selling and lowest selling stocks on a day basis. The result shows a huge difference in the number of scans which is the main motive of this study.