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
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
Multidimensional Binary Search Trees in Database Applications
IEEE Transactions on Software Engineering
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
Fast global k-means clustering using cluster membership and inequality
Pattern Recognition
Quantization-based clustering algorithm
Pattern Recognition
On the efficiency of swap-based clustering
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Improving the performance of k-means for color quantization
Image and Vision Computing
On-line multi-stage sorting algorithm for agriculture products
Pattern Recognition
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In this paper, we present a modified filtering algorithm (MFA) by making use of center variations to speed up clustering process. Our method first divides clusters into static and active groups. We use the information of cluster displacements to reject unlikely cluster centers for all nodes in the kd-tree. We reduce the computational complexity of filtering algorithm (FA) through finding candidates for each node mainly from the set of active cluster centers. Two conditions for determining the set of candidate cluster centers for each node from active clusters are developed. Our approach is different from the major available algorithm, which passes no information from one stage of iteration to the next. Theoretical analysis shows that our method can reduce the computational complexity, in terms of the number of distance calculations, of FA at each stage of iteration by a factor of FC/AC, where FC and AC are the numbers of total clusters and active clusters, respectively. Compared with the FA, our algorithm can effectively reduce the computing time and number of distance calculations. 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.