Research And Improvement On APRIORITID Algorithm Of Association Rules

Association rules mining technology is one of the most widely used techniques in data mining areas, it aims to extract inner and uneasily found links. The paper first introduces the related theory of association rules mining technology, analyses the exist association rule algorithm-AprioriTid algorithm, and summarizes the exist two major bottleneck problems of current association rules mining: (1) For large database, the early formation of candidate Tid table may be larger than the original transaction database. (2) Every generating Tid table records many meaningless item sets that cause heavy computer storage burden.This paper aims at above two bottleneck problems and presents an improvement algorithm that based on transaction compression and item sets compression, meanwhile, it presents a method that can simply and efficiently clip middle produced frequent item sets. Then it uses simple examples to analysis the theory examine. Secondly, the paper uses UCI standard test dataset to test and compare the improved algorithm performance, the improved algorithm saves 20%-40% time than the original algorithm, it can greatly improve the algorithm performance. Finally, the improved algorithm is used in the college scores database, through the analysis and extracting of the college grades data, it concludes some valuable advices.