5 papers
Few-Shot Resampling for Scalable Statistically-Sound Data Mining
Leonardo Pellegrina, Fabio Vandin
A key step in knowledge discovery is the evaluation of data mining results. In several applications, including pattern mining, graph analysis, and others, this step includes the ev…
Sampling Random Graphs from the Colored Configuration Model
Leonardo Pellegrina
A fundamental step in knowledge discovery is statistically assessing data mining results. In network analysis, such evaluation compares the outcome of a given procedure with the ou…
Fast Percolation Centrality Approximation with Importance Sampling
Antonio Cruciani, Leonardo Pellegrina
In this work we present PercIS, an algorithm based on Importance Sampling to approximate the percolation centrality of all the nodes of a graph. Percolation centrality is a general…
Scalable Rule Lists Learning with Sampling
Leonardo Pellegrina, Fabio Vandin
Learning interpretable models has become a major focus of machine learning research, given the increasing prominence of machine learning in socially important decision-making. Amon…
Efficient Discovery of Significant Patterns with Few-Shot Resampling
Leonardo Pellegrina, Fabio Vandin
Significant pattern mining is a fundamental task in mining transactional data, requiring to identify patterns significantly associated with the value of a given feature, the target…