Performance Evaluation of Association Rule Mining with Enhanced Apriori Algorithm Incorporated with Artificial Bee Colony Optimization Algorithm

Abstract

Abstract:In data mining, association rules are produced in view of solid relations and regularities existing among the variables in extensive exchanges. These association rules go for extricating connections, frequent patterns and associations among the item sets in exchanges. Association rules are connected for use in different zones, for example, media transmission, hazard administration and so forth. One such territory where the association rules are vital is the stock market. In stock marketing, picking the right stock relies on upon the genuine stock quality and the capacity to pick the stock is urgent as it impacts the profit of investors. In this work, the proposed technique develops a novel and effective way to deal with producing optimal stock rules to help in the stock market prediction by utilizing Enhanced Apriori algorithm and Artificial Bee Colony Optimization (ABC) algorithm. In the result profit of PP, EMA, ROC and RSI for min-support 3, 4, 5 and 6 individually in rule mining for HCL stock market dataset is appeared with correlation of GA-Apriori algorithm, AGA-Enhanced Apriori algorithm and ABC-Enhanced Apriori algorithm. From this Artificial Bee Colony (ABC) optimization algorithm is performed when contrasting and different strategies and grant that strong association rules to be produced.

Pattern Classification of Stock Price Moving

“…What is more, Asadifar S and Kahani M have put forward their improved research achievement that they implement semantic data mining for prediction of stock price moving data by using association rules in 2017 [20]. In 2017, Selvanambi R and Natarajan J have applied their improved Apriori algorithm on performance evaluation which is used for association rule mining on the stock price moving data [21]. SOM algorithm is one of the artificial neural networks that imitates the function of the human brains that categorize items by groups and it is also well known as a self-organization algorithm.…”