Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Datasets

Abstract: We investigate the use of biased sampling according to the density of the dataset, to speed up the operation of general data mining tasks, such as clustering and outlier detection in large multidimensional datasets. In density-biased sampling, the probability that a given point will be included in the sample depends on the local density of the dataset. We propose a general technique for density-biased sampling that can factor in user requirements to sample for properties of interest, and can be ...