Smart Profitable Solutions with Recommendation Framework

Abstract

Abstract: Discovering the frequent patterns in transactional databases is one of the crucial functionalities of apriori algorithm. Apriori algorithm is an algorithm which works on the principle of association rule mining. It is a dynamic and skillful algorithm used for discovering frequent patterns in a database, hence proving out to be efficient and important in data mining. Apriori algorithm finds associations between different sets of data. Every different set of data has a collective number of items and is called a transaction. The accomplishment of apriori is the set of rules that expose us how often any particular item or a set of items is contained in a set of data. In our proposed system, to provide efficiency, our basic aim is to implement apriori algorithm by setting up a threshold value and a varying support count which will act as a filter for our recommendation data. We can adjust the threshold value in order to increase or decrease the accuracy of the system. We have used apriori algorithm keeping in mind, its application in retailing industry and its capability of computing and handling large datasets and especially for the purpose of market basket analysis. The use of apriori algorithm along with analytical tools can provide insights into data and help the user in management and decision making provided that the user feeds the system in a correct way. Our aim is to provide user with recommendations which would ultimately help them in improving their business operations.