The Algorithm Research Of Association Rules Mining

Data Mining is an information processing technology, which is developing very fast in recent years. Using data mining, people can abstract information and knowledge from a great deal of data which is incomplete, noisy, dark and random.The information and knowledge we got was ignored and had not been known before but potentially useful.Association rule mining is an important sub-branch of the Data Mining, which mines interesting association or correlation relationships among a large set of data items. Association rules are considered interesting if they satisfy both a minimum support threshold and a minimum confidence threshold. Association rule mining has become a hot research topic in recent years, and it has been used widely in selective marketing, decision analysis and business management. Association rule mining algorithms are the core contents in the area. So far, there are several famous typical algorithms.Firstly, this paper generally discusses the Data Mining, including the concepts and the pattern of the Data Mining, the application and development trend of the Data Mining. Secondly, this thesis deeply researches the association rule mining algorithm, which is important in the Data Mining. It analyses Apriori algorithm, which is classic in the association rule mining algorithms, and the improved technology of Apriori algorithm, and summarizes problems existing in Apriori algorithm. Based on Apriori algorithm, this paper presents a more efficient algorithm for association rule mining. The new algorithm adopts a unique way to generate the candidate itemsets. This is a high efficient algorithm which can mine all the frequent itemsets by scanning the source database only once.