An Algorithm For Mining Complementarity-Alternative Relationship Based On Frequent Itemsets

The database and the information'technology have highly developing and widespread applicating in many domain Since the 1980s, a large number of historical data have not only accumulated in real life and commercial applications,but aslo increase in Geometric progression's way.The data Storing in the large database system contain mass of information which are advantageous for the decision makers to make astute decision.Due to lacking a powerful data analytical application,these data became "the data'grave" that many people weren't interested to mention. The dexision maker always make a decison basing on their intuition and experiences.Thus the decisions usually incapability in applied realm.These data contain many worthy informatios and resources,and they should guide the decision-maker in providing an implicit information. But,they can not act well in fact.How to mine informations from the large database becomes the demand for many applied realm. In this case, the data mining technology emerges with a new subject.It provides a basis by many different ways for many domain,such as commerce strategy,science,and so on, and makes the information technology fast development.Frequent item sets mining is the first step in generating association rules,its mining efficiency is directly related to the efficiency of generating association rules,But It has been expanded by the number of scanning dataset and efficiency of computing support in mining frequent itemsets. Based the algorithm for ming Top-N closed frequent itemsets, proposed by shariq bashir,this article first proposed an algorithm for mining frequent itemsets. It only need to scan dataset once using bit-vector to represent dataset.The process of mining frequent itemsets is represented by the process of creating the node of enumeration tree.While it is creating the tailset of the enumeration itemsets, using PBR-Index of the enumeration itemsets of head,the algorithm computes the support of the new itemsets combined by the head itemsets and one item in the tailset of the node. Simultaneously,we introduce the breadth expansion pruning strategy and region-index skiming strategy.Thus it can fastly find out all of the frequent itemsets,and effectively improve the the algorithm's efficiency.Because the frequent itemsets might include a great deal of rule that consumer were not interested,and if there is no further analysis or realm knowledge, the association rule can not apply directly to Prediction.So how to eliminate the rule that consumers is not interested in the frequent pattern is also the bottleneck in mining frequent pattern. Based the first algorithm for mining frequent itemsets,we also proposed another algorithm for mining the frequent itemsets's correlationship. It computes the relevant computation between the items including in frequent itemsets to find their correlationship.and eliminates the influence of the noise datas, demonstrates their complementary- alternative relationship to the policy-maker by CAG,so it is advantageous for the policy-maker to make reasonable and accurate judgment. Results suggested that the algorithm for mining complement-alternative relationship in this article is more efficient and more precise than frequent itemsets in expressing informations.