Data classification attempts to assign a category or a class label to an unknown data object based on an available similar data set with class labels already assigned. K nearest neighbor (KNN) is a widely used classification technique in data mining. KNN assigns the majority class label of its closest neighbours to an unknown object, when classifying an unknown object. The computational efficie...

Automated brain segmentation methods with a good precision and accuracy are required to detect subtle changes in brain volumes over time in clinical applications. However, the ability of established methods such as SIENA, US and kNN to estimate brain volume change have not been compared on the same data, nor been evaluated with ground-truth manual segmentations. We compared measurements of brai...

High-dimensional problems arising from robot motion planning, biology, data mining, and geographic information systems often require the computation of k nearest neighbor (knn) graphs. The knn graph of a data set is obtained by connecting each point to its k closest points. As the research in the above-mentioned fields progressively addresses problems of unprecedented complexity, the demand for...

This paper proposes SV-kNNC, a new algorithm for k-Nearest Neighbor (kNN). This algorithm consists of three steps. First, Support Vector Machines (SVMs) are applied to select some important training data. Then, k-mean clustering is used to assign the weight to each training instance. Finally, unseen examples are classified by kNN. Fourteen datasets from the UCI repository were used to evaluate ...

The k-Nearest-Neighbours (kNN) is a simple but effective method for classification. The major drawbacks with respect to kNN are (1) its low efficiency being a lazy learning method prohibits it in many applications such as dynamic web mining for a large repository, and (2) its dependency on the selection of a “good value” for k. In this paper, we propose a novel kNN type method for classificatio...

The potassium sodium niobate, K0.5Na0.5NbO₃, solid solution (KNN) is considered as one of the most promising, environment-friendly, lead-free candidates to replace highly efficient, lead-based piezoelectrics. Since the first reports of KNN, it has been recognized that obtaining phase-pure materials with a high density and a uniform, fine-grained microstructure is a major challenge. For this rea...

Approximate kNN (k-nearest neighbor) techniques using binary hash functions are among the most commonly used approaches for overcoming the prohibitive cost of performing exact kNN queries. However, the success of these techniques largely depends on their hash functions’ ability to distinguish kNN items; that is, the kNN items retrieved based on data items’ hashcodes, should include as many true...