Sampling-based data mining algorithms: Modern techniques and case studies

Abstract

Sampling a dataset for faster analysis and looking at it as a sample from an unknown distribution are two faces of the same coin. We discuss the use of modern techniques involving the Vapnik-Chervonenkis (VC) dimension to study the trade-off between sample size and accuracy of data mining results that can be obtained from a sample. We report two case studies where we and collaborators employed these techniques to develop efficient sampling-based algorithms for the problems of betweenness centrality computation in large graphs and extracting statistically significant Frequent Itemsets from transactional datasets. © 2014 Springer-Verlag.