A distance based classification is one of the popular methods for classifying instances using a point-to-point distance based on the nearest neighbour or k-NEAREST NEIGHBOUR (k-NN). The representation of distance measure can be one of the various measures available (e.g. Euclidean distance, Manhattan distance, Mahalanobis distance or other specific distance measures). In this paper, we propose ...

WC prcscnt a characterization of heuristic evaluation functions Hhich unities their trcatmcnt in single-agent problems and twoperson games. ‘l‘hc central result is that a useful heuristic function is one which dctcrmincs the outcome of a search and is invariant along a solution path. ‘I‘his local chnractcrization of heuristics can hc used to predict the cffcctivcncss of given heuristics and to ...

For a set T of n points (terminals) in the plane, a Manhattan network on T is a network N(T ) = (V, E) with the property that its edges are horizontal or vertical segments connecting points in V ⊇ T and for every pair of terminals, the network N(T ) contains a shortest l1-path between them. A minimum Manhattan network on T is a Manhattan network of minimum possible length. The problem of findin...

Given a set S of n points in the plane, we deene a Manhattan Network on S as a rectilinear network G with the property that for every pair of points in S, the network G contains the shortest rectilinear path between them. A Minimum Manhattan Network on S is a Manhattan network of minimum possible length. A Manhattan network can be thought of as a graph G = (V; E), where the vertex set V corresp...

We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space. In this space, the Euclidean distance between any two nodes approximates the length of the shortest path between them in the given graph. Later, at runtime, a shortest path between any two nodes can be computed using A* search with the Euclidean distances as heurist...

In this paper, we study the spatial node stationary distribution of two variations of the Random Waypoint (in short, RWP) mobility model. In particular, differently from the RWP mobility model, that connects source to destination points by straight lines, our models make use of Manhattan or (more realistically) Bezier paths. We provide analytical results for the spatial node stationary distribu...

The huge amount of daily generated data in smart cities has called for more effective storage, processing, and analysis technologies. A significant part this are streaming (i.e., time series data). Time similarity or dissimilarity measuring represents an essential critical task several mining machine learning algorithms. Consequently, a distance measure that can extract the similarities differe...

For a set T of n points (terminals) in the plane, a Manhattan network on T is a network N(T ) = (V,E) with the property that its edges are horizontal or vertical segments connecting points in V ⊇ T and for every pair of terminals, the network N(T ) contains a shortest l1-path between them. A minimum Manhattan network on T is a Manhattan network of minimum possible length. The problem of finding...