In this paper, we propose a new algorithm to make a novel spatial image transformation. The proposed approach aims to reduce the bit depth used for image storage. The basic technique for the proposed transformation is based of the modulus operator. The goal is to transform the whole image into multiples of predefined integer. The division of the whole image by that integer will guarantee that t...

The k-means method is a widely used technique for clustering points in Euclidean space. While it is extremely fast in practice, its worst-case running time is exponential in the number of data points. We prove that the k-means method can implicitly solve PSPACE-complete problems, providing a complexity-theoretic explanation for its worst-case running time. Our result parallels recent work on th...

Domain analysis tries to reuse software in an e ective way New methodologies are start ing to be able to automate the process in di erent degrees with the construction of a domain model for each problem The general process is divided into several phases One of the most di cult tasks is the generation of the relationships which have to be de ned between the components in the domain In this paper...

Hartigan’s method for k-means clustering is the following greedy heuristic: select a point, and optimally reassign it. This paper develops two other formulations of the heuristic, one leading to a number of consistency properties, the other showing that the data partition is always quite separated from the induced Voronoi partition. A characterization of the volume of this separation is provide...

T HE THEORY developed in the statistical literature for the method of k-means can be applied to the study of optimal k-level vector quantizers. In this paper, I describe some of this theory, including a consistency theorem (Section II) and a central lim it theorem (Section IV) for k-means cluster centers. These results help to explain the behavior of optimal vector quantizers constructed from l...