Mining Frequent Itemsets using Patricia Tries

We present a depth-first algorithm, PatriciaMine, that discovers all frequent itemsets in a dataset, for a given support threshold. The algorithm is main-memory based and employs a Patricia trie to represent the dataset, which is space efficient for both dense and sparse datasets, whereas alternative representations were adopted by previous algorithms for these two cases. A number of optimizations have been introduced in the implementation of the algorithm. The paper reports several experimental results on real and artificial datasets, which assess the effectiveness of the implementation and show the better performance attained by PatriciaMine with respect to other prominent algorithms.

• Type: Conference
• Year: 2003
• Where: FIMI
• Authors: Andrea Pietracaprina, Dario Zandolin
• Keywords: Data Mining, Depth-first Algorithm, FIMI 2003, Patricia Trie, Sparse Datasets