Research On Algorithm Of Mining Of Negative Association Rules

Knowledge Discovery in Databases (KDD) is a very active field among the subjects of the databases technique, the artificial intelligence, etc. Data Mining is an important step of KDD, can discovery knowledge from the databases. The knowledge can be understood by users and is interesting, valid and potentially useful.Data Ming is recognized as the hot topic in the database research field, and has received increasing attention by researchers, which is defined as the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data sources. Mining association rules from large databases plays an essential role in many data mining tasks and has broad applications.In many fields, only mining standard association rules are not enough. Mining negative association rules is also required. Negative association rules allow the negative itemsets appear in the association rules, expand the defining of association rules, and then increase the ability of description of association rules.The paper introduces an algorithm of mining of negative association rules, which based on correlation and interesting threshold. The algorithm mines not only positive association rules but also negative association rules at the same time. At present, the research of negative association rules focus on mining interesting negative association rules in positive frequent itemsets. The thesis introduces a method of creating negative frequent itemsets in which can mining negative association rules.The evaluation criteria are based on support and confidence in existing association rule mining algorithms. But, in many time, the mined association rules with high support and confidence are useless. At the same time, the evaluation criterion does not consider whether the corresponding rules with negative item are useful or not, when the positive item rules with high support and confidence are useless. The thesis introduces the interesting threshold into association rules, which will be used to prune the useless association rules together with the threshold of support and confidence. And based on the predecessors' improved definition of...