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Vector Quantizationby Lazy Pairwise Nearest Neighbor Method

Timo Kaukoranta, Pasi Fränti, Olli Nevalainen, Vector Quantizationby Lazy Pairwise Nearest Neighbor Method. TUCS Technical Reports 180, Turku Centre for Computer Science, 1998.

Abstract:

Clustering of a data set can be done by the well-known Pairwise Nearest Neighbor (PNN) algorithm. The algorithm is conceptionally very simple and gives high quality solutions. A drawback of the method is the relatively large running time of the original (exact) implementation. Recently, an efficient version of the exact PNN algorithm has been introduced in literature. In this paper we give a faster implementation of this algorithm. The idea is to postpone the updating of the nearest neighbor information in order to reduce the number of cluster distance calculations. Correctness of the algorithm follows from the monotony of the cluster distances. Practical tests show that the new organization of the algorithm decreases the running time of PNN by ca. 35 per cent.

<p>Submitted to IEEE Transactions on Signal Processing

<p>Contact authors for the complete report.

BibTeX entry:

@TECHREPORT{tKaFrNe98a,
  title = {Vector Quantizationby Lazy Pairwise Nearest Neighbor Method},
  author = {Kaukoranta, Timo and Fränti, Pasi and Nevalainen, Olli},
  number = {180},
  series = {TUCS Technical Reports},
  publisher = {Turku Centre for Computer Science},
  year = {1998},
  keywords = {Vector quantization, Codebook generation, Clustering algorithm, Pairwise nearest neighbor algorithm},
  ISBN = {952-12-0220-3},
}

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