Abstract
In this paper, we discuss the problem domain of high-dimensional nearest neighbor retrieval. We give a brief overview on existing approaches based on convex cluster shapes. Subsequently, we sketch the advantage of concave cluster geometries and introduce three concave cluster proposals. Furthermore, we put our concave clustering approaches into a context with index compression techniques. Finally, an outlook on our ongoing work concludes this paper.
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References
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Balko, S. (2002). Compression Techniques Based on Concave Cluster Geometries for Efficient High-Dimensional Nearest Neighbor Retrieval. In: Chaudhri, A.B., Unland, R., Djeraba, C., Lindner, W. (eds) XML-Based Data Management and Multimedia Engineering — EDBT 2002 Workshops. EDBT 2002. Lecture Notes in Computer Science, vol 2490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36128-6_43
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DOI: https://doi.org/10.1007/3-540-36128-6_43
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