Research And Application Of Apriori Algorithm Based On Cluster And Compression Matrix

From the data mining theory appeared in last 1980'S,it has developed speedily.Data mining is becoming much more important as the amount of databases keeps increasing.Many Researchers have shown great interest in data mining.The application of data mining is also extended to various fields.Data mining emerged as a rapidly growing technology in order to extract valuable information and knowledge in large volumes of data.Association rules mining is an important technology of data mining,which discovers previously unknown and interesting relationships among attributes in the large databases. Many scholars have made improvements to Apriori algorithm. This article is on the basis of improved algorithm, we design an algorithm which based on cluster and compression matrix-CCM_Apriori algorithm.The works of this dissertation are as follows:(1)Describes and summarizes data warehouse and data mining techniques,then focuses on the basic concepts of cluster analysis and association rules,ideas,and on the representative algorithms,at last analyzes their advantages and disadvantages.(2)Used cluster and relationship operation of the Boolean vector,we design an algorithm which based on compression matrix and cluster.The algorithm reduces the size of the database based on affairs compression and cluster.lt scans the database only once and does not directly generate candidate itemsets geted frequent itemsets.Thus raises the algorithm operation efficiency effectively.(3) Using the Data Warehouse and CCM_Apriori algorithm in the original system of Educational Administration Management system in college.According to the students history elective records and test scores,we could forecast the name of elective courses and the number of the students,and realize the elective course decision support system.lt provides decision support for opening elective courses in the college.