Association Rule Mining Tax System

In the early 20th century, even in the mid-20th century, no company accounts, ordering the data records and file cabinets can be more than the sum of scores of millions of bytes. Today, the largest company database capacity is measured in trillions of bytes. The accumulation of huge amounts of data for data mining applications in the new space, the data mining market share is growing, more and more large and medium enterprises began to use data mining to analyze the company's data to decision support, data mining is becoming their competition in the market invincible magic weapon.This thesis is on a detailed analysis of Apriori algorithm, Apriori algorithm summarized the advantages and disadvantages, the performance bottleneck for the Apriori algorithm, to analyze optimization algorithm and its characteristics. A+ algorithm is a fast algorithm for mining frequent itemsets, the algorithm will apply knowledge of mathematics frequent itemset mining using the vector inner product operation, and gradually reduce the matrix, eventually obtained frequent item sets. The proposed algorithm is simple and efficient search without candidate itemsets generated, only one database scan. The algorithm is applied to the tax system for mining association rules, and tax data on the A+ based algorithm compared with Apriori algorithm. The results show that, The running time of A+ is less than Apriori algorithm.FG algorithm is based on efficient tree service rules for mining association rules algorithm is FP-growth algorithm for the structural conditions in the superset of FP-tree and the establishment of service rules test model is used to illustrate the problem. The transaction chain rule tree into rules, the rules on the use of FG algorithm chain to achieve the service rules tree model structure, structural form of the rules after the completion of most of the redundant rules have been removed; and then re-FG algorithm using filtering technology to remove redundant rules out all non-redundant association rules. FG does not need to find frequent items, direct to find out association rules, methods and more flexible. The algorithm is applied to tax inspectors, the same FG based on the data set on the algorithm and FP-Growth algorithm for analysis. Experimental results show that, running time of FG is less than FP-Growth algorithm.Paper first introduces the basic concepts of association rules, classification, mining step, analysis research and application of association rules, then introduce a classical algorithm for mining association rules algorithm and the existing improved Apriori algorithm; then the association rule algorithm and improved algorithm A+ FG algorithm is introduced, analyzed and compared by experiments. Next comes the data mining in the tax system status, tax data, characteristics, and A+ algorithm and FG algorithm are used in the concrete application of the tax system.