In a finite metric space (F, #) a (discrete) diametrical problem concerns a quest ion like "How large can a subset A of F with #(a, a I) <_ 5 for all a, a I E A be and what are the opt imal configurations?" For systems of subsets with the union function d V or binary sequences with the Hamming distance dH the diametrical problems have been solved by Ka tona [3] and Klei tman [4], respectively. ...

The resource discovery mechanism is a hot topic issue in grid environments and It has great impact on the efficiency of the resource sharing and cooperative computing. Based on the domain and resource routing nodes, a grid resource discovery model of multilayer overlay network was given in this paper. And on this basis, using a linear combination of the block distance and chessboard distance in...

Path selection in multihomed nodes can be enhanced by optimization techniques that consider multiple criteria. With NP-Hard problems, MADM techniques have the flexibility of including any number of benefits or costs criteria and are open regarding the functions that can be employed to normalize data or to determine distances. TOPSIS uses the Euclidean distance (straight line) while DiA employs ...

It is well known that pairs of dimensions that are processed holistically integral dimensions normally combine with a Euclidean metric, whereas pairs of dimensions that are processed analytically separable dimensions most often combine with a city-block metric. This paper extends earlier research regarding information integration in that it deals with complex stimuli consisting of both dimensio...

This paper investigates the skeletonization problem using parallel thinning techniques and proposes a new one-pass parallel asymmetric thinning algorithm (OPATA 8). Wu and Tsai presented a one-pass parallel asymmetric thinning algorithm (OPATA 4) that implemented 4-distance, or city block distance, skeletonization. However, city block distance is not a good approximation of Euclidean distance. ...

The cognitive framework of conceptual spaces [3] provides geometric means for representing knowledge. A conceptual space is a highdimensional space whose dimensions are partitioned into so-called domains. Within each domain, the Euclidean metric is used to compute distances. Distances in the overall space are computed by applying the Manhattan metric to the intra-domain distances. Instances are...

Fast indexing in time sequence databases for similarity searching has attracted a lot of research recently. Most of the proposals, however, typically centered around the Euclidean distance and its derivatives. We examine the problem of multimodal similarity search in which users can choose the best one from multiple similarity models for their needs. In this paper, we present a novel and fast i...

An instance of a p-median problem gives n demand points. The objective is to locate p supply points in order to minimize the total distance of the demand points to their nearest supply point. p-Median is polynomially solvable in one dimension but NP-hard in two or more dimensions, when either the Euclidean or the rectilinear distance measure is used. In this paper, we treat the p-median problem...

Radial Basis Function (RBF) networks typically use a distance function designed for numeric attributes, such as Euclidean or city-block distance. This paper presents a heterogeneous distance function which is appropriate for applications with symbolic attributes, numeric attributes, or both. Empirical results on 30 data sets indicate that the heterogeneous distance metric yields significantly i...