An Association Rule Mining Model for Finding the Interesting Patterns in Stock Market Dataset

Abstract

In these days, stock market forecasting is one of the most interesting issues, which has gained a more attention due to vast profits. To precisely predict the price of share and making profits has been always challenging task since the longest period of time. This has engrossed the interest and attention of stock brokers, economists and applied researchers. Traditional methods like Fundamental analysis, Technical analysis, and Regression methods are not suitable for this task because these tools and techniques are based on totally different analytical approaches and requiring highly expertise and justification in the area. In this sequence, Association Rule Mining is one of the most interesting research areas for finding the associations, correlations among items in a database. It can discover all useful patterns from stock market dataset. The aim of this research study is to help stock brokers, investors so that they can earn maximum profits for each trading.

Determination of Association Rules with Market Basket Analysis: Application in the Retail Sector

“…As a result of the analysis of the association with the Apriori algorithm, five prominent rules were identified [23]. In their study performed in the stock market database, Kamley et al aimed to determine the investment tools that would bring maximum profits to the stock market and investors and determine the appropriate time [24]. Kaur and Kang , suggested an algorithm that would perform association rule mining on dynamic data.…”