Sequential sampling algorithms: unified analysis and lower bounds

Abstract

Sequential sampling algorithms have recently attracted interest as a way to design scalable algorithms for Data mining and KDD processes. In this paper, we identify an elementary sequential sampling task (estimation from examples), from which one can derive many other tasks appearing in practice. We present a generic algorithm to solve this task and an analysis of its correctness and running time that is simpler and more intuitive than those existing in the literature. For two specific tasks, frequency and advantage estimation, we derive lower bounds on running time in addition to the general upper bounds.