Fast PNN Using Partial Distortion Search
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Fast PNN-based Clustering Using K-nearest Neighbor Graph
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Enhancing interactive particle visualization with advanced shading models
APGV '06 Proceedings of the 3rd symposium on Applied perception in graphics and visualization
Fast Agglomerative Clustering Using a k-Nearest Neighbor Graph
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative shrinking method for clustering problems
Pattern Recognition
Parallel algorithm for codebook generation with multi-step approach and hierarchical clustering
MATH'08 Proceedings of the American Conference on Applied Mathematics
Improvement of the fast exact pairwise-nearest-neighbor algorithm
Pattern Recognition
Evolutionary computation using reinforced learning on image compression
ISTASC'08 Proceedings of the 8th conference on Systems theory and scientific computation
Evolutionary computation using reinforced learning on image compression
SSIP'08 Proceedings of the 8th conference on Signal, Speech and image processing
Reduced parallel PNN algorithm for PC grid systems
Proceedings of the 2009 ACM symposium on Applied Computing
Implementation of codebook generation algorithm on a campus PC Grid
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
Fast agglomerative clustering using information of k-nearest neighbors
Pattern Recognition
An agglomerative clustering algorithm using a dynamic k-nearest-neighbor list
Information Sciences: an International Journal
Comparative study on VQ with simple GA and ordain GA
ACMOS'07 Proceedings of the 9th WSEAS international conference on Automatic control, modelling and simulation
Comparison of clustering methods: A case study of text-independent speaker modeling
Pattern Recognition Letters
Density-based hierarchical clustering for streaming data
Pattern Recognition Letters
PatZip: pattern-preserved spatial data compression
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Straightforward implementation of the exact pairwise nearest neighbor (PNN) algorithm takes O(N3) time, where N is the number of training vectors. This is rather slow in practical situations. Fortunately, much faster implementation can be obtained with rather simple modifications to the basic algorithm. In this paper, we propose a fast O(τN2) time implementation of the exact PNN, where τ is shown to be significantly smaller than N, We give all necessary data structures and implementation details, and give the time complexity of the algorithm both in the best case and in the worst case. The proposed implementation achieves the results of the exact PNN with the same O(N) memory requirement