The k-nearest neighbour (kNN) problem appears in many different fields of computer science, such as computer animation and robotics. In crowd simulation, kNN queries are typically used by a collision-avoidance method to prevent unnecessary computations. Many different methods for finding these neighbours exist, but it is unclear which will work best in crowd simulations, an application which is...

K nearest neighbor algorithm is one of the most frequently used techniques in data mining for its integrity and performance. Though the KNN algorithm is highly effective in many cases, it has some essential deficiencies, which affects the classification accuracy of the algorithm. First, the effectiveness of the algorithm is affected by redundant and irrelevant features. Furthermore, this algori...

The identification and discrimination of 2 closely related and morphologically similar species of Gyrodactylus, G. salaris and G. thymalli, were assessed using the statistical classification methodologies Linear Discriminant Analysis (LDA) and k-Nearest Neighbours (KNN). These statistical methods were applied to morphometric measurements made on the gyrodactylid attachment hooks. The mean estim...

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...

The present work addresses binary classification by use of the k-nearest neighbors (kNN) classifier. Among several assets, it belongs to intuitive majority vote classification rules and also adapts to spatial inhomogeneity, which is particularly relevant in high dimensional settings where no a priori partitioning of the space seems realistic. However the performance of the kNN classifier crucia...

In mobile communication, spatial queries pose a serious threat to user location privacy because the location of a query may reveal sensitive information about the mobile user. In this paper, study approximate k nearest neighbor (kNN) queries where the mobile user queries the location-based service (LBS) provider about approximate k nearest points of interest (POIs) on the basis of his current l...

The sparse representation based classifier (SRC) is a classical representation method for classification. The solution of SRC is obtained by l1 norm minimization, which can not obtain the closed form solution. Thus, the computational complexity of SRC is a little high. The collaborative representation classifier (CRC) is another classical method for classification. The solution of CRC is obtain...

k-nearest-neighbour (KNN) has been widely used as an effective classification model. In this paper, we summarize three main shortcomings confronting KNN and then single out three categories of approaches for overcoming its three main shortcomings. After reviewing some algorithms in each category, we presented a hybrid algorithm called dynamic k-nearest-neighbour naive Bayes with attribute weigh...

K-nearest neighbor (KNN) is one of the accepted classification tool . Classfication is one of the foremost machine-learning tools used in field of medical data mining. However, one of the most complicated tasks in developing a KNN is determining the optimal number of nearest neighbors, which is usually obtained by repeated experiments for different values of K, till the minimum error rate is ac...