Discovering cyclic generalized association rules from transaction datbases can reveal the relationship of differ-ent levels of the taxonomies and display cyclic variations over time.Information about such variations i...Discovering cyclic generalized association rules from transaction datbases can reveal the relationship of differ-ent levels of the taxonomies and display cyclic variations over time.Information about such variations is great use of better identifying trends in associations and forecast-ing.Because cyclic rules are quite sensitive to a littlenoise,this paper uses the noise-ratio as the criterion of i-dentifing cydclic itemsets for dealing with the problem and utilizes the cycle-pruning technique to reduce the comput-ing time of the data mining process by exploiting the real-tionship between the cycle and generalized frequent item-sets.The paper gives the algorithm of mining cyclic gen-eralized itemsets(CGI).Experiment shows that the CGI algorithm can efficiently yield results.展开更多
文摘Discovering cyclic generalized association rules from transaction datbases can reveal the relationship of differ-ent levels of the taxonomies and display cyclic variations over time.Information about such variations is great use of better identifying trends in associations and forecast-ing.Because cyclic rules are quite sensitive to a littlenoise,this paper uses the noise-ratio as the criterion of i-dentifing cydclic itemsets for dealing with the problem and utilizes the cycle-pruning technique to reduce the comput-ing time of the data mining process by exploiting the real-tionship between the cycle and generalized frequent item-sets.The paper gives the algorithm of mining cyclic gen-eralized itemsets(CGI).Experiment shows that the CGI algorithm can efficiently yield results.
