ProSecCo: Progressive Sequence Mining with Convergence Guarantees
Sacha Servan-Schreiber, Matteo Riondato, Emanuel Zgraggen · 2018
We present PROSECCO, an algorithm for the progressive mining of frequent sequences from large transactional datasets: it processes the dataset in blocks and outputs, after having analyzed each block, a high-quality approximation of the collection of frequent sequences. These intermediate results have strong probabilistic approximation guarantees and the final output is the exact collection of frequent sequences. Our correctness analysis uses the Vapnik-Chervonenkis (VC) dimension, a key concept from statistical learning theory. The results of our experimental evaluation of PROSECCO on real and artificial datasets show that it produces fast-converging high-quality results almost immediately. Its practical performance is even better than what is guaranteed by the theoretical analysis, and it can even be faster than existing state-of-the-art non-progressive algorithms.