A k-nearest neighbor (kNN) query determines the k nearest points, using distance metrics, from a given location. An all k-nearest neighbor (AkNN) query constitutes a variation of a kNN query and retrieves the k nearest points for each point inside a database. Their main usage resonates in spatial databases and they consist the backbone of many location-based applications and not only. In this w...

According to the defects of KNN(K-Nearest Neighbor) algorithm and SVM(Support Vector Machine) algorithm in tracking a moving target such the large consumption and the low accuracy of target tracking error, a tracking model of moving target is proposed based on the combination of KNN algorithm and SVM algorithm with minimum distance optimization. First categories divided according to the princip...

Our final solution (RMSE=0.8712) consists of blending 107 individual results. Since many of these results are close variants, we first describe the main approaches behind them. Then, we will move to describing each individual result. The core components of the solution are published in our ICDM'2007 paper [1] (or, KDD-Cup'2007 paper [2]), and also in the earlier KDD'2007 paper [3]. We assume th...

K-Nearest Neighbor (KNN) classification and regression are two widely used analytic methods in predictive modeling and data mining fields. They provide a way to model highly nonlinear decision boundaries, and to fulfill many other analytical tasks such as missing value imputation, local smoothing, etc. In this paper, we discuss ways in SAS R © to conduct KNN classification and KNN Regression. S...

A k-nearest neighbor (kNN) query determines the k nearest points, using distance metrics, from a given location. An all k-nearest neighbor (AkNN) query constitutes a variation of a kNN query and retrieves the k nearest points for each point inside a database. Their main usage resonates in spatial databases and they consist the backbone of many location-based applications and not only. In this w...

An investigation has been conducted on two well known similarity-based learning approaches to text categorization: the k-nearest neighbor (k-NN) classifier and the Rocchio classifier. After identifying the weakness and strength of each technique, a new classifier called the kNN model-based classifier (kNNModel) has been proposed. It combines the strength of both k-NN and Rocchio. A text categor...

In this paper, we develop a novel Distance-weighted k -nearest Neighbor rule (DWKNN), using the dual distance-weighted function. The proposed DWKNN is motivated by the sensitivity problem of the selection of the neighborhood size k that exists in k -nearest Neighbor rule (KNN), with the aim of improving classification performance. The experiment results on twelve real data sets demonstrate that...

An investigation has been conducted on two well known similarity-based learning approaches to text categorization. This includes the k-nearest neighbor (kNN) classifier and the Rocchio classifier. After identifying the weakness and strength of each technique, we propose a new classifier called the kNN model-based classifier by unifying the strengths of k-NN and Rocchio classifier and adapting t...