The widespread use of location-aware devices has led to countless location-based services in which a user query can be arbitrarily complex, i.e., one that embeds multiple spatial selection and join predicates. Amongst these predicates, the k-Nearest-Neighbor (kNN) predicate stands as one of the most important and widely used predicates. Unlike related research, this paper goes beyond the optimi...

Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbours (kNN) method is widely used for short-term traffic forecasting. However, the self-adjustment of kNN parameters has been a problem due to dynamic traffic characteristics. This paper proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-a...

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...

Lead free potassium sodium niobate (KNN) piezoceramics were synthesized via conventional solid state sintering route. Nano and micron WO3 were separately added to KNN through ball-milling. Dielectric and piezoelectric properties of samples sintered in the temperature range of 1110°-1145°C were measured by precision LCR-meter and APC d33-meter devices. The results revealed that micron WO3 partic...

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...

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...

In pattern recognition, a kind of classical classifier called k-nearest neighbor rule (kNN) has been applied to many real-life problems because of its good performance and simple algorithm. In kNN, a test sample is classified by a majority vote of its k-closest training samples. This approach has the following advantages: (1) It was proved that the error rate of kNN approaches the Bayes error w...

Incompleteness is one of the problematic data quality challenges in real-world machine learning tasks. A large number studies have been conducted for addressing this challenge. However, most existing focus on classification task and only a limited symbolic regression with missing values exist. In work, new imputation method incomplete proposed. The aims to improve both effectiveness efficiency ...

 Abstract—KNN classification finds k nearest neighbors of a query in training data and then predicts the class of the query as the most frequent one occurring in the neighbors. This is a typical method based on the majority rule. Although majority-rule based methods have widely and successfully been used in real applications, they can be unsuitable to the learning setting of skewed class distr...

Collaborative Filtering (CF) is the most popular choice when implementing personalized recommender systems. A classical approach to CF is based on K-nearest-neighborhood (KNN) model, where the precondition for making recommendations is the KNN construction for involved entities. However, when building KNN sets, there exits the dilemma to decide the value of K --a small value will lead to poor r...