Clustering Analyser For Pattern Recognition
H.D. Cheng, C. Tong · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1987
Cluster analysis is a generic name for a variety of mathematical methods that can be used to classify a given data set. By using the cluster analysis the people try to understand a set of data and to reveal the structure of the data. Clustering technologies find very important applications in the disciplining of pattern recognition and image processing. They are very useful for unsupervised pattern classification and image segmentation. This paper presents a VLSI cluster analyser for implementing the squared-error clustering technique using extensive pipelining and parallel computation capabilities. The proposed cluster analyser could perform one pass of the squared-error algorithm (which includes finding the squared distances between every cluster center, assigning each pattern to its closest cluster center and recomputing the cluster centers) in 0(N+M+K) time units, where M is the dimension of the feature vector, N is the number of sample patterns, and K is the desired number of clusters. And it will need 0(N x M xK) time units, if a uniprocessor is used. The algorithm partition problem is also studied in this paper.