Research And Implementation Of An Algorithms For Mining Constrained Maximum Frequent Itemsets

With growth of database technology and popularity of network technology,a lot of collecting data was increased rapidly.The capacity of storing data was enlarged hugely around the world.Data explosion and knowledge scarcity is an urgent phenomenon in information society.Data mining is one of effective method to tackle the problem, which is a process of extracting useful information and identifying valid, novel, potentially useful, and ultimately understandable patterns in data from large volumes of raw data.Therefore, study on data mining technique is of important practical meaning. Based on data mining of association rules for the research field, association rule data mining algorithms are detailed analysis and research.The work of author mainly focuses on two aspects in the following:On one hand,an incremental updating algorithm for mining association rules based on the change of database is proposed. Discovering frequent itemsets is a key problem in data mining association rules.The frequent itemsets is a set of all items that satisfies a minimum support and a minimum confidence in a given transactional database. With the addition and subtraction of the database, different frequent itemsets will be produced.Under the variance of database, how to utilize mined useful information and realize maintenance of frequent itemsets , this question is needed researching;On the other hand,constrained maximal frequent itemsets mining algorithm is proposed. Constraint condition is applied to the mining algorithm ,which reduces the number of candidate itemsets and increases the efficiency of algorithm. The experimental result shows that this algorithm has effective and operational. To a certain extent, a lot of irrelative and worthless association rules is reduced.