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.

If one defines the distance between two points as the Manhattan distance (the sum of the horizontal distance along streets and the vertical distance along avenues) then one can define a city as being optimal if the average distance between pairs of points is a minimum. In this paper a nonlinear differential equation for the boundary curve of such a city is determined. The problem solved here is...

Sentiment analysis is a data processing to recognize topics that people talk about and their sentiments toward the topics, one of which in this study large-scale social restrictions (PSBB). This aims classify negative positive by applying K-Nearest Neighbor algorithm see accuracy value 3 types distance calculation are cosine similarity, euclidean, manhattan for Indonesian language tweets (PSBB)...

In this paper, we rewrite the Minimal-Connected-Component (MCC) model in 2-D meshes in a fully-distributed manner without using global information so that not only can the existence of a Manhattan-distance-path be ensured at the source, but also such a path can be formed by routing-decisions made at intermediate nodes along the path. We propose the MCC model in 3-D meshes, and extend the corres...

Given a set T of n points in IR, a Manhattan Network G is a network with all its edges horizontal or vertical segments, such that for all p, q ∈ T , in G there exists a path (named a Manhattan path) of the length exactly the Manhattan distance between p and q. The Minimum Manhattan Network (MMN) problem is to find a Manhattan network of the minimum length, i.e., the total length of the segments...

This paper describes the comparative study of performance between the existing distance metrics like Manhattan, Euclidean, Vector Cosine Angle and Modified Euclidean distance for finding the similarity of complexion by calculating the distance between the skin colors of two color facial images. The existing methodologies have been tested on 110 male and 40 female facial images taken from FRAV2D...

Given a set T of n points in IR, a Manhattan Network G is a network with all its edges horizontal or vertical segments, such that for all p, q ∈ T , in G there exists a path (named a Manhattan path) of the length exactly the Manhattan distance between p and q. The Minimum Manhattan Network problem is to find a Manhattan network of the minimum length, i.e., the total length of the segments of th...

1 Similarity Metrics Many recommendation algorithms employ some form of similarity metric in the generation of ratings predictions. Similarity metrics are often associated with some form of distance measure. Definition 1.0.1. Let δ be a function δ : R × R → R. Let x,y, z ∈ R. Then δ is a distance measure if it satisfies the following four properties. (d1) δ(x,y) ≥ 0 (no negative distances). (d2...

The design a novel location aided routing protocol with the concept of baseline and distance minimization is presented in this paper. The protocol has been implemented successfully for a vehicular adhoc network and Manhattan model is utilized. The vehicular movement used a new concept of communication using minimization of distance from base line. The base line was drawn from source to destinat...