Construction of periodic temporal association rules in data mining
Ru Miao, Xiajiong Shen · 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery · 2010
Association rules are the very valuable kind of law in data mining. The fitness of time is seldom illustrated by traditional association rules, which losses a number of useful implicit rules. On the basis of further study of other association rules mining algorithms, this paper has developed Apriori-extended mining periodic temporal association rules (MPTAR) according to the especial periodicity of data. The test on a group of financial data shows that the method is useful and efficient. It is more significant for improving the theory and implementation of temporal association rules in data mining.