Improving the Efficiency of the Apriori Algorithm for Mining Association Rules

• Main Authors: Chiao Yin Yao, 姚喬尹
• Other Authors: Chui Cheng Chen
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/65311017636604670428

碩士 === 南台科技大學 === 資訊管理系 === 98 === With the development of information technology, enterprises have a lot of way to get information and can use this technology store about a lot of enterprise’s transaction or record in data base. How to find the useful information in database has become the subject which the enterprises pay attention. Association rules technology is generally in data mining. Based on the Internet Technology development and the globalization of business, the transaction database of enterprise is constantly changing all the time, and in order to keep the accuracy of exploring result in dynamic database, the traditional explore method in order to keep the information accuracy so it unavoidable must to exploring information again constantly; Because generated too many redundant candidate itemsets so it causes too many times to scan the database; Is need to scan the redundant transaction data because there is not recognize this items belong to which transaction. In order to preserve the accuracy when mining the dynamic database, we need repeatedly scan database. This is above the traditional Apriori algorithm to mining association rules of the weakness in the dynamic database. This research is based on Apriori Algorithm to improve its process. This paper proposed an improve algorithms. The new algorithm is to transform database from horizontal to vertical. This can be avoided scan redundant of Transaction data. Any item count just need to scan two transactions in data base so as to increase mining efficiency. And this is improved from Apriori generate candidate itemsets process. That can avoid generate too many candidate itemset and can increase mining efficiency again. And propose appropriate methods to update this algorithm so as to this algorithms can use in dynamic database in real-time and correctly, to fit in with the business needs and provide immediate and accurate to the important decision-making.