Analysis of Association Rule Mining for Business Transaction Data

Abstract

Association rule mining represents a data mining technique and its goal is to find interesting association or correlation relationships among a large set of data items. With massive amount of data continuously being collected and stored in databases, many companies are becoming interested in mining association rules from their databases to increase their profits from large amount of transaction data. In this paper, Apriori Algorithm analyses sales data for extracting frequent itemsets. Sales data are used from Stationery Store. It is clarify how we can find association rules from large amount of transaction data. According to the interestingness measures, such as support, confidence and correlation, this system can also support for decision making process for a market expert. This system with implemented by C# and MSAccess Database 2003 on .NET platform.