Abstract
In this paper, we present our research on similarity search and clustering problems. Similarity search problems define the distances between data points and a given query point Q, efficiently and effectively selecting data points which are closest to Q. Clustering algorithms separate data points into different groups, in a way that data points in the same group have high similarity and data points from different groups are different from each other. In this paper, we explore the meaning of clusters from a new perspective, and propose an approach to reshape the clusters based on K nearest neighbor search results. The reconstructed clusters can help improve the performance of the following K nearest search process.
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Shi, Y., Graham, B. (2012). An Approach to Reshaping Clusters for Nearest Neighbor Search. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_8
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DOI: https://doi.org/10.1007/978-3-642-32639-4_8
Publisher Name: Springer, Berlin, Heidelberg
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