An efficient agglomerative clustering algorithm using a heap
Pattern Recognition
Vector quantization and signal compression
Vector quantization and signal compression
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Finite-state vector quantization for waveform coding
IEEE Transactions on Information Theory
On the computational complexity of the LBG and PNN algorithms
IEEE Transactions on Image Processing
Fast and memory efficient implementation of the exact PNN
IEEE Transactions on Image Processing
An efficient encoding algorithm for vector quantization based on subvector technique
IEEE Transactions on Image Processing
Fast-searching algorithm for vector quantization using projection and triangular inequality
IEEE Transactions on Image Processing
Fast exact k nearest neighbors search using an orthogonal search tree
Pattern Recognition
Fast k-nearest neighbors search using modified principal axis search tree
Digital Signal Processing
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
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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.