Fast k-NN classifier for documents based on a graph structure
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
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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.