Improvement of the fast exact pairwise-nearest-neighbor algorithm

Pairwise-nearest-neighbor (PNN) is an effective method of data clustering, which can always generate good clustering results, but with high computational complexity. Fast exact PNN (FPNN) algorithm proposed by Franti et al. is an effective method to speed up PNN and generates the same clustering results as those generated by PNN. In this paper, we present a novel method to improve the FPNN algorithm. Our algorithm uses the property that the cluster distance increases as the cluster merge process proceeds and adopts a fast search algorithm to reject impossible candidate clusters. Experimental results show that our proposed method can effectively reduce the number of distance calculations and computation time of FPNN algorithm. Compared with FPNN, our proposed approach can reduce the computation time and number of distance calculations by a factor of 24.8 and 146.4, respectively, for the data set from three real images. It is noted that our method generates the same clustering results as those produced by PNN and FPNN.

[1]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Shen-Chuan Tai,et al.  Two fast nearest neighbor searching algorithms for image vector quantization , 1996, IEEE Trans. Commun..

[3]  Jeng-Shyang Pan,et al.  An efficient encoding algorithm for vector quantization based on subvector technique , 2003, IEEE Trans. Image Process..

[4]  MAGDALINI EIRINAKI,et al.  Web mining for web personalization , 2003, TOIT.

[5]  Jim Z. C. Lai,et al.  Image restoration of compressed image using classified vector quantization , 2002, Pattern Recognit..

[6]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[7]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[8]  Robert M. Gray,et al.  Finite-state vector quantization for waveform coding , 1985, IEEE Trans. Inf. Theory.

[9]  Sergios Theodoridis,et al.  Pattern Recognition, Third Edition , 2006 .

[10]  V. Weerackody,et al.  Design of vector quantizers using simulated annealing , 1988 .

[11]  Pasi Fränti,et al.  Fast and memory efficient implementation of the exact PNN , 2000, IEEE Trans. Image Process..

[12]  Olli Nevalainen,et al.  Vector Quantizationby Lazy Pairwise Nearest Neighbor Method , 1998 .

[13]  James McNames,et al.  A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  N. de Freitas,et al.  On-line probabilistic classification with particle filters , 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

[15]  Daben Liu,et al.  Online speaker clustering , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[16]  Shu-Chuan Chu,et al.  Equal-average Equal-variance Equal-norm Nearest Neighbor Codeword Search Algorithm Based on Ordered Hadamard Transform , 2005 .

[17]  Sergios Theodoridis,et al.  Pattern Recognition , 1998, IEEE Trans. Neural Networks.

[18]  Takio Kurita,et al.  An efficient agglomerative clustering algorithm using a heap , 1991, Pattern Recognit..

[19]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

[20]  Philip Ogunbona,et al.  On the computational complexity of the LBG and PNN algorithms , 1997, IEEE Trans. Image Process..

[21]  Yi-Ching Liaw,et al.  Fast-searching algorithm for vector quantization using projection and triangular inequality , 2004, IEEE Transactions on Image Processing.

[22]  William Equitz,et al.  A new vector quantization clustering algorithm , 1989, IEEE Trans. Acoust. Speech Signal Process..