The pairwise nearest neighbor method (PNN) generates the clustering of a given data set by a sequence of merge steps. We propose an alternative solution for the merge-based approach by introducing an iterative shrinking method. The new method removes the clusters iteratively one by one until the desired number of clusters is reached. Instead of merging two nearby clusters, we remove one cluster by reassigning its data vectors to the neighbor clusters. We retain the local optimization strategy of the PNN by always removing the cluster that increases the cost function least. We give six alternative implementations, which all outperform the PNN in quality.

Straightforward implementation of the exact pairwise nearest neighbor (PNN) algorithm takes O(N3) time, where N is the number of training vectors. This is rather slow in practical situations. Fortunately, much faster implementation can be obtained with rather simple modifications to the basic algorithm. In this paper, we propose a fast O(tauN2) time implementation of the exact PNN, where tau is shown to be significantly smaller than N, We give all necessary data structures and implementation details, and give the time complexity of the algorithm both in the best case and in the worst case. The proposed implementation achieves the results of the exact PNN with the same O(N) memory requirement.

This paper introduces a new method for reducing the number of distance calculations in the generalized Lloyd algorithm (GLA), which is a widely used method to construct a codebook in vector quantization. Reduced comparison search detects the activity of the code vectors and utilizes it on the classification of the training vectors. For training vectors whose current code vector has not been modified, we calculate distances only to the active code vectors. A large proportion of the distance calculations can be omitted without sacrificing the optimality of the partition. The new method is included in several fast GLA variants reducing their running times over 50% on average.

This paper introduces a new method for reducing the number of distance calculations in the generalized Lloyd algorithm (GLA), which is a widely used method to construct a codebook in vector quantization. The reduced comparison search detects the activity of the code vectors and utilizes it on the classification of the training vectors. For training vectors whose current code vector has not been modified, we calculate distances only to the active code vectors. A large proportion of the distance calculations can be omitted without sacrificing the optimality of the partition. The new method is included in several fast GLA variants reducing their running times over 50% on average.

We propose a new iterative algorithm for the generation of a codebook in vector quantization. The algorithm starts with an initial codebook that is improved by a sequence of merge and split operations. By merging small neighboring clusters additional resources (code vectors) will be released. These extra code vectors can be reallocated by splitting large clusters. The process can be iterated until no improvement is achieved in the distortion of the codebook. Experimental results show that the proposed method performs well in comparison to other tested methods, including the Generalized Lloyd algorithm (GLA) and two hierarchical methods.

We consider the clustering problem in the case where the distances between elements are metric and both the number of attributes and the number of clusters are large. In this environment the genetic algorithm approach gives high quality clusterings, but at the expense of long running time. Three new and efficient crossover techniques are introduced her. The hybridization of the genetic algorithm and k-means algorithm is discussed.

Block truncation coding (BTC) is a lossy moment preserving quantization method for compressing digital gray-level images. Its advantages are simplicity, fault tolerance, the relatively high compression efficiency and good image quality of the decoded image. Several improvements of the basic method have been recently proposed in the literature. In this survey we will study the basic algorithm and its improvements by dividing it into three separate tasks; performing quantization, coding the quantization data and coding the bit plane. Each phase of the algorithm will be analyzed separately. On the basis of the analysis, a combined BTC algorithm will be proposed and the comparisons to the standard JPEG algorithm will be made.