An Efficient Lattice-Based Approach for Generator Mining
Pham Quang Huy, Truong Chi Tin
Abstract
Mining frequent closed itemsets and theirs corresponding generators seem to be the most effective way to mine frequent itemsets and association rules from large datasets since it helps reduce the risks of low performance, big storage and redundancy. However, generator mining has not been studied as much as frequent closed itemsets mining and it has not reached the ultra-optimization yet. In this paper, we consider the problem of enumerating generators from the lattice of frequent closed itemsets as the problem of “distributing M machines to solve N jobs” in order to introduce a close and legible point of view. From this, it is easy to infer some interesting mathematical results to solve the problem easily. Our proposed algorithm, GDP, can efficiently find all generators in very low complexity without duplicated or useless consideration. Experiments show that our approach is reasonable and effective.
Keyword
Generator, minimal generator, generator mining, lattice of closed frequent itemsets, lattice-based algorithm, dynamic programming algorithm, parallel algorithm.