Download Metric All-k-Nearest

Metric All-k-Nearest-Neighbor Search
Abstract:
An all-k-nearest-neighbor (AkNN) query finds from a given object set O, k nearest
neighbors for each object in a specified query set Q. This operation is common in
many applications such as GIS, data mining, and image analysis. Although it has
received much attention in the Euclidean space, there is little prior work on the
metric space. In this paper, we study the problem of AkNN retrieval in metric
spaces, termed metric AkNN(MAkNN) search, and propose efficient algorithms for
supporting MAkNN queries with arbitrary k value. Our methods utilize dynamic
disk-based metric indexes (e.g., M-tree), employ a series of pruning rules, take
advantage of grouping, reuse, pre-processing, and progressive pruning
techniques, require no detailed representations of objects, and can be applied as
long as the distance metric satisfies the triangle inequality. In addition, we extend
our approaches to tackle metric self-AkNN (MSAkNN) search, a natural variation
of MAkNN queries, where the query set Q is identical to the object set O.
Extensive experiments using both real and synthetic data sets demonstrate,
compared with state-of-the-art euclidean AkNN, MAkNN, and MSAkNN
algorithms, the performance of our proposed algorithms and the effectiveness of
our presented techniques.