ECLAT Algorithm's Study And Application

In recent years, with development of computer technology, data management technology was successfully applied, and enterprise information continuously improved. Vast amounts of data accumulate in the database on various application fields. Using data mining techniques can access to the right, interesting and potentially valuable knowledge from the large-scale data. Mining Association Rules is one of the significant research methods, and has important theoretical value and broad application prospects.At present, the association rules mining is subject to considerable concern, most of the association rules mining is based on Apriori algorithm and Fp-growth algorithm. In this paper the frequent itemsets'mining was deeply analyzed, and systematically classified. On the basis of their database's form, the frequent itemsets'mining algorithm can be divided two parts: divide database and plumb database.Through analyzed the classical representative algorithms: Apriori algorithm and Fp-growth algorithm, their advantages and shortcoming were pointed out. Then we deeply analyzed the Eclat algorithm, by analyzing the Eclat algorithm, it shows that when the Tidsets is generated their quantity is very large, this step consumed a lot of time and memories. In order to resolve this question, a new improvement algorithm—Declat was presented. The algorithm applyed the method of division to Eclat, it reduced the Tidset's quantity when operate intersecting; then proposed a priority constraint, to reduce the local frequent itemsets'quantity. The algorithm's efficiency was improved. Experiments show that, the algorithm the paper presented has better efficiency than the Eclat algorithm.The existing algorithm of mining negative association rules is very few, and most of them are based on Apriori algorithm which need to scan data sets for many times, and generated a large number of candidates frequent itemsets. On the basis of research results which presented by the related scholars at home and abroad, this paper presented a frequent itemsets mining algorithm which based on the Diffset plumb database. This algorithm used the Eclat algorithm .So it has no need to repeatedly scanning the database, and didn't generation large amount of candidate itemsets. Experiments show that, the algorithm the paper presents has better efficiency than similar existing mining algorithm.