Compression Techniques Based on Concave Cluster Geometries for Efficient High-Dimensional Nearest Neighbor Retrieval

Authors:
Sören Balko
Affiliations:
-
Venue:
EDBT '02 Proceedings of the Worshops XMLDM, MDDE, and YRWS on XML-Based Data Management and Multimedia Engineering-Revised Papers
Year:
2002

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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.