A High-Dimensional Access Method for Approximated Similarity Search in Text Mining

In this paper, a new access method for very high-dimensional data space is proposed. The method uses a graph structure and pivots for indexing objects, such as documents in text mining. It also applies a simple search algorithm that uses distance or similarity based functions in order to obtain the k-nearest neighbors for novel query objects. This method shows a good selectivity over very-high dimensional data spaces, and a better performance than other state-of-the-art methods. Although it is a probabilistic method, it shows a low error rate. The method is evaluated on data sets from the well-known collection Reuters corpus version 1 (RCV1-v2) and dealing with thousands of dimensions.