The k-nearest neighbour (KNN) technique is a simple yet effective method for classification. In this paper, we propose an efficient weighted nearest neighbour classification algorithm, called PINE, using vertical data representation. A metric called HOBBit is used as the distance metric. The PINE algorithm applies a Gaussian podium function to set weights to different neighbours. We compare PIN...

We classify the neighbour-transitive codes in Johnson graphs J(v, k) of minimum distance at least 3 which admit a neighbour-transitive group of automorphisms that is an almost simple 2-transitive group of degree v and does not occur in an infinite family of 2-transitive groups. The result of this classification is a table of 22 codes with these properties. Many have relatively large minimum dis...

Urban residential neighbour noise is ubiquitous but its effects are relatively under-researched. Neighbour noise is difficult to measure and is often locally controlled under nuisance, rather than environmental health, regulations. We analyze the health effects of residential noise annoyance using a high quality longitudinal survey of over 5000 adults in the Netherlands between 2007 and 2013. W...

The nearest neighbour (NN) and k-nearest neighbour (kNN) classi cation rules have been widely used in pattern recognition due to its simplicity and good behaviour. Exhaustive nearest neighbour search can become unpractical when facing large training sets, high dimensional data or expensive similarity measures. In the last years a lot of NN search algorithms have been developed to overcome those...

When all the individuals in a social group can be easily identified, one of the simplest measures of social interaction that can be recorded is nearest-neighbour identity. Many field studies use sequential scan samples of groups to build up association metrics using these nearest-neighbour identities. Here, I describe a simple technique for identifying clusters of associated individuals within ...

A graph has the neighbour-closed-co-neighbour, or ncc property, if for each of its vertices x, the subgraph induced by the neighbour set of x is isomorphic to the subgraph induced by the closed non-neighbour set of x. As proved by Bonato, Nowakowski [5], graphs with the ncc property are characterized by the existence of perfect matchings satisfying certain local conditions. In the present artic...

Abstruct-The finite sample performance of a nearest neighbor classifier is analyzed for a two-class pattern recognition problem. An exact integral expression is derived for the m-sample risk R, given that a reference m-sample of labeled points is available to the classifier. The statistical setup assumes that the pattern classes arise in nature with fixed a priori probabilities and that points ...

In this paper we propose and analyze a novel method for automatic stock trading which combines technical analysis and the nearest neighbor classification. Our first and foremost objective is to study the feasibility of the practical use of an intelligent prediction system exclusively based on the history of daily stock closing prices and volumes. To this end we propose a technique that consists...

Superconducting phase diagram of the extended Hubbard model supplemented with interaction and hopping terms exceeding nearest neighbour distance in range is analysed systematically at different band-filling and temperature values in a mean-field approximation. The obtained results clearly underline the importance of next-nearest neighbour terms in developing the main superconducting properties ...