Development of Association Rule Mining with Efficient Positive and Negative Rules

Abstract

Association rule mining (ARM) is one of the most researched areas of data mining and recently from the database community it has received much attention. In the marketing and retail communities, they are proven to be quite useful in the other more diverse fields. On this area some of the previous research is done, the concept behind association rules are provided at the beginning followed by an overview to some research. The advantages and limitations are concluded with an inference. There are several algorithms, in frequent pattern mining. The classical and most famous algorithm is Apriori. To find frequent item sets and association between different items sets is the objective of using Apriori algorithm, i.e. association rule. In this paper author considers data (Online Seller transaction data) and tries to obtain the results using weak a data mining tool. To find out best combination, association rule algorithm are used of different attributes in any data.