Research of the Optimization of a Data Mining Algorithm Based on an Embedded Data Mining System

At present most of the data mining systems are independent with respect to the database system, and data loading and conversion take much time. The running time of the algorithms in a data mining process is also long. Although some optimized algorithms have improved it in different aspects, they could not improve the efficiency to a large extent when many duplicate records are available in a database. Solving the problem of improving the efficiency of data mining in the presence of many coinciding records in a database, an Apriori optimized algorithm is proposed. Firstly, a new concept of duplication and use is suggested to remove and count the same records, in order to generate a new database of a small size. Secondly, the original database is compressed according to the users' requirements. At last, finding the frequent item sets based on binary coding, strong association rules are obtained. The structure of the data mining system based on an embedded database has also been designed in this paper. The theoretical analysis and experimental verification prove that the optimized algorithm is appropriate and the algorithm application in an embedded data mining system can further improve the mining efficiency.