Legislation arising from health and environmental concerns has intensified research into finding suitable alternatives to lead-based piezoceramics. Recently, solid solutions based on sodium potassium niobate (K,Na)NbO3 (KNN) have become one of the globally-important lead-free counterparts, due to their favourable dielectric and piezoelectric properties. This data article provides information on...

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...

In supervised learning problems, global and local learning algorithms are used. In contrast to global learning algorithms, the prediction of a local learning algorithm in a testing point is only based on training data which are close to the testing point. Every global algorithm such as support vector machines (SVM) can be localized in the following way: in every testing point, the (global) lear...

Software Architecture is important factor for the development of complex and big software system. Software Architecture Decomposition is an important part in software design. Software clustering is used to cluster functions of similar type in one cluster and other are in other cluster. Kmean is the base of the clustering but it has some limitations. Many clustering methods are used for decompos...

The k nearest neighbor (kNN) search is a computationally intensive application critical to fields such as image processing, statistics, and biology. Recent works have demonstrated the efficacy of k-d tree based implementations on multi-core CPUs. It is unclear, however, whether such tree based implementations are amenable for execution in high-density processors typified today by the graphics p...

This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training set. The results of the weak classifiers are combined using the weighted sum rule. The proposed sol...

An automated wood recognition system is designed to classify tropical wood species.The wood features are extracted based on two feature extractors: Basic Grey Level Aura Matrix (BGLAM) technique and statistical properties of pores distribution (SPPD) technique. Due to the nonlinearity of the tropical wood species separation boundaries, a pre classification stage is proposed which consists ofKme...

Texture-based computer-aided diagnosis (CADx) of microcalcification clusters is more robust than the state-of-art shape-based CADx because the performance of shape-based approach heavily depends on the effectiveness of microcalcification (MC) segmentation. This paper presents a texture-based CADx that consists of two stages. The first one characterizes MC clusters using texture features from gr...

In recent years, research efforts to extend linear metric learning models to handle nonlinear structures have attracted great interests. In this paper, we propose a novel nonlinear solution through the utilization of deformable geometric models to learn spatially varying metrics, and apply the strategy to boost the performance of both kNN and SVM classifiers. Thin-plate splines (TPS) are chosen...

Associating documents to relevant categories is critical for effective document retrieval. Here, we compare the well-known k-Nearest Neighborhood (kNN) algorithm, the centroid-based classifier and the Highest Average Similarity over Retrieved Documents (HASRD) algorithm, for effective document categorization. We use various measures such as the micro and macro F1 values to evaluate their perfor...