Finding Representatives via Nearest Neighbor Based Binarization and Frequent Pattern Mining

Abstract

We propose to find representative data points from continuous data via a two-step procedure: We first binarize data points based on the nearest neighbor search, followed by performing frequent pattern mining on the binarized data. Since frequent patterns correspond to combinations of data points shared by many other data points as their neighbors, they are expected to well summarize the entire dataset. We empirically show that representative data points detected by our method have competitive quality with random sampling in the classification scenario.