The Apriori Algorithm Based On Data Mining

Our capabilities of both generating and collecting data have been increasing rapidly in the last several decades. Contributing factors include the widespread use of bar codes for most commercial products, the computerization of many business, scientific, and government transactions, and advances in data collection tools ranging from scanned text and image platforms to satellite remote sensing, systems; In addition, popular use of the World Wide Web as a global information system has flooded us with a tremendous amount of data and information. This explosive growth in stored data has generated an urgent need for new techniques and automated tools that can intelligently assist us in transforming the vast amounts of data into useful information and knowledge.Data mining is a promising and flourishing frontier in database systems and new database applications. Data mining, also popularly referred to as knowledge discovery in database (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored in large database, data warehouses, and other massive information repositories. It is one of the international advanced directions in the field of database and information decision, and association rules mining is one of keys of Data mining.Association rules mining finds interesting association or correlation relationships among a large set of data items. With massive' amounts of data continuously being collected and stored, many industries are becoming interested in mining association rules from their databases. The discovery of interesting association relationships among huge amounts of business transaction records can help in many business decision making processes, such as catalog design, cross-marketing, and loss-leader analysis.This paper studies the algorithms of association rules mining. All the work can be concluded as follows:1. To provides a survey of the study of in the data mining generation.2. In this dissertation, on the base of the Apriori algorithm research, this dissertation optimized and improved the Apriori algorithm, and presents an efficient algorithm. In the field of Data mining research, Apriori algorithm is a promising algorithm. The main challenge and key problem that association rules is applied to data mining is enormous data, so efficiency is very important.3. To finish the system analysis, design and development, including the initiative design, detailed design and software design. The dissertation is under the environment of DG_CIMS including Order subsystem, Consign subsystem, Price subsystem, and Funds subsystem mainly. The system that has been developed is running successfully.4. To build up the system prototype of data warehouse under the environment of the order subsystem of the sale system.5. The new algorithm gives some experimental results in DGCIMS. On the base of the data mining and association rules research, the improved algorithm verifies the performance in DGCIMS.