This paper describes Apriori Algorithm,which is an Algorithm of mining Boolean associate rule frequent itemset based on transaction database and associate rule. The applied program is implemented in ext-PROLOG language.
In addition, based on PLM's data analysis operation, the associated rules mining method is chosen to analyze the potential factor of the substandard product appearance, the clustering analysis arithmetic is applied to construe the relation between the logistics data of the products and products profit, and the decision tree arithmetic based information entropy is used to analyze and predict the customer's latent energy of value for enterprises.
该方案采用基于最大—最小规范化的属性构造方法和基于径向基函数神经网络的数据聚类方法进行数据预处理,结合PLM系统数据分析业务,采用基于高频模式树的项约束关联规则发现方法分析产生次品的潜在因素; 采用聚类分析算法CLIMB(clustering algorithm based on subspace)分析产品的物流数据与产品利润之间的关系;
The structure of operation optimi-zation based on data mining is established and the fuzzy association rule mining algorithm is introduced to find the operation optimization target value to guide the operation in power plants.
Class Association Rule (CAR) based classification is a growing topic in recent datamining study for its high interpretability and accuracy.
The paper proposes a novel clustering algorithm CFSBC based on closed frequent itemsets derived from association rule mining, which can get the clustering attributes with high efficiency.
A fast distributed algorithm for association rule mining based on binary coding mapping relation
Association rule mining is an important issue in data mining.