Profitable Association Mining Rules based on Casual Survey Approach Using Apriori Algorithm

Anjali Sharma, Neeraj Kumar Choudhary

Abstract: In this technology specific era; everyone in industry look for smart methods to get profit in business. The biggest contribution is given by the introduction of Data Mining branch of Computer Science Engineering. The huge size of data is monitored; compressed and mined to get interesting facts. Different approaches are introduced day by day so as to formulate hidden associations between data. Following to which; new association rules are being generated. The base idea to find most occurring events was provided in Apriori Algorithm. In this paper we will analyse classical Apriori algorithm. We will introduce the concept of Casual Transaction database that includes direct survey from users i. e; how they trend to go for selections. We will propose an itea to integrate Casual transaction database with Real transaction database (what customer really does). We will analyse frequent itemset and association rules. We will then try to fit this approach with Apriori Algorithm. Accordingly we will keep this paper for our vision towards implementation of this approach and for our future work area.

Keywords: Association rules, Data Mining, Apriori Algorithm, frequent itemset, Casual Survey Approach