Mining algorithm for maximal frequent itemsets based on improved FP-tree
Chuanjian Yang · Journal of Computer Applications · 2012
In order to reduce the repeated traversal times of path in the FP-tree,the conditional pattern bases of all frequent 1-itemsets in the FP-tree need to be saved in the existing algorithms.Concerning this problem,in the new algorithm,the data structure of FP-tree was improved that only the conditional pattern bases were saved which were constituted by the items in the path from every leaf node' parents to the root in the FP-tree,and the storage space of the conditional pattern bases was reduced.After studying search space and the method of data representation in the algorithm for mining maximal frequent itemsets,the pruning and compression strategies were developed through theoretical analysis and verification,which could decrease the search space and the scale of FP-tree.Finally,the new algorithm was compared with NHTFPG algorithm and FpMAX algorithm respectively in terms of accuracy and efficiency.The experimental results show that the new FP-tree algorithm saves the required conditions for model-based storage space more than 50% than NHTFPG algorithm,and the efficiency ratio improves by 2 to 3 times than FpMAX algorithm.