An Efficient Mining Algorithm for Maximal Weighted Frequent Patterns Based on WIdT-Trees

Qibing Qin, Long Tan

Abstract: As processed data is relatively dense or the support is small in weighted frequent patterns mining process, the number of frequent patterns which meet the conditions will be exponential growth, and mining all frequent patterns will need too much computation. Hence, mining the maximal weighted frequent patterns containing all frequent patterns has less calculation, and it has more utility value. Aiming at the process of maximal weighted frequent patterns mining, an efficient algorithm, based on WIdT-Trees, is proposed to discover maximal weighted frequent patterns. In the algorithm, WIdT-Tree is optimized from WIT-Tree. The dTidset strategy is used to calculate the weighted support of frequent k-itemsets, and the nodes with equal extended weighted support are pruned off in order to reduce the computational cost and decrease the search space complexity. Algorithms are tested and compared on real and synthetic datasets and experimental results show that our algorithm is more efficient and scalable.

External IDs:dblp:conf/ideal/QinT16