An n-point metric space (X,D) can be represented by an n × n table specifying the distances. Such tables arise in many diverse areas. For example, consider the following scenario in microbiology: X is a collection of bacterial strains, and for every two strains, one is given their dissimilarity (computed, say, by comparing their DNA). It is difficult to see any structure in a large table of num...

Given a set S of n points in the plane, a Manhattan network on S is a (not necessarily planar) rectilinear network G with the property that for every pair of points in S the network G contains a path between them whose length is equal to the Manhattan distance between the points. A Manhattan network on S can be thought of as a graph G = (V,E) where the vertex set V corresponds to the points of ...

This paper presents the results of classifying Arabic text documents using the N-gram frequency statistics technique employing a dissimilarity measure called the “Manhattan distance”, and Dice’s measure of similarity. The Dice measure was used for comparison purposes. Results show that N-gram text classification using the Dice measure outperforms classification using the Manhattan measure.

Euclidean distance matrix completion problem aims at reconstructing the low dimensional geometry structure of nodes given only a few pairwise distances between nodes [1]. This problem arises in many applications including networks and machine learning where much information of points is laking. For instance, in sensor networks, due to the constraints of energy and communication radius, sensor n...

Abstract. Purpose: Chili is an agricultural product that has several varieties and in great demand. It can be consumed directly or processed first. This study aims to classify the types of chili using color shape features. The are divided into five classes: cayenne pepper, green chili, big red curly chili. classification method was evaluated three parameters: precision, recall, accuracy.Methods...