Research And Improvement On The Algorithms Of Mining Association Rules

With the development of science and technology, the rapid growth of data, there is an urgent need for a method to deal with the data. Data mining comes into being in such a background and it is the theory and method that mining valuable information from the implicit data.Date Mining is one of the most famouse and cutting-edge researches of artificial intelligence and machine learning. Association rule is the most important part of data mining.In the process of mining association rules, frequent itemsets mining is the core of the mining process. How to effectively mining frequent itemsets has long been a focus of attention of researchers. In practice, however, the explosive growth of data and the huge number of frequently itemsets hinder the wide use of frequent item sets. Therefore, how to optimize the algorithm and compress frequent itemsets has become an important direction of current research.This paper firstly introduces the concept and basic mining algorithms about association rules and frequent itemsets, then introduces the related technologies of itemsets compression in detail, and analyzes several effective compression algorithms of frequent itemsets briefly.Finally, this paper proposes two algorithms based on nodesets for mining Top-k frequent itemsets,the TBN algorithm and the TCBN algorithm.(1) This paper propose the TBN algorithm.The TBN algorithm is based on nodesets,using a Top-k-rank table, abandoned artificial for the minimum support intervention, so that the minimum support is dynamic. only two traversal database operations, the use of generating the Top-k mining method for mining frequent itemsets. Analysis and experimental comparison shows that the TBN algorithm consumes less time and space in different degree for different dataset.(2) This paper propose the TCBN algorithm for mining Top-k frequent closed itemsets.The TCBN algorithm presents two strategies for quickly mining frequent closed itemsets,which are on the basis of the new data structure of POC-Tree and the nature of closed itemsets,and reduce the number of candidate by combining two methods for closed detection to ensure that the results are all closed by Top-k-rank table. Analysis and experimental comparison shows that the superiority of the TCBN algorithm in terms of time and space.This paper has carried on the contrast experiment wide of the proposed algorithms which have better performance, especially on dense datasets. The proposed algorithm aims to solve the frequent item sets mining algorithm in practical application. The huge size of item sets and the number of results is difficult to set the minimum support problem, these researches about mining frequent itemsets provide an effective solution to the practical problems.