Vector quantization and signal compression
Vector quantization and signal compression
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Accelerating exact k-means algorithms with geometric reasoning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
A fast VQ codebook generation algorithm using codeword displacement
Pattern Recognition
Finite-state vector quantization for waveform coding
IEEE Transactions on Information Theory
A fast exact GLA based on code vector activity detection
IEEE Transactions on Image Processing
Fast full search equivalent encoding algorithms for image compression using vector quantization
IEEE Transactions on Image Processing
A comparison of several vector quantization codebook generation approaches
IEEE Transactions on Image Processing
Pattern Recognition
Quantization-based clustering algorithm
Pattern Recognition
Expert Systems with Applications: An International Journal
Hemodialysis key features mining and patients clustering technologies
Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Rough clustering using generalized fuzzy clustering algorithm
Pattern Recognition
Fast K-means clustering using deletion by center displacement and norms product (CDNP)
Pattern Recognition and Image Analysis
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In this paper, we present a fast k-means clustering algorithm (FKMCUCD) using the displacements of cluster centers to reject unlikely candidates for a data point. The computing time of our proposed algorithm increases linearly with the data dimension d, whereas the computational complexity of major available kd-tree based algorithms increases exponentially with the value of d. Theoretical analysis shows that our method can reduce the computational complexity of full search by a factor of SF and SF is independent of vector dimension. The experimental results show that compared to full search, our proposed method can reduce computational complexity by a factor of 1.37-4.39 using the data set from six real images. Compared with the filtering algorithm, which is among the available best algorithms of k-means clustering, our algorithm can effectively reduce the computing time. It is noted that our proposed algorithm can generate the same clusters as that produced by hard k-means clustering. The superiority of our method is more remarkable when a larger data set with higher dimension is used.