The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. Past experience has shown that the optimal choice of K depends upon the data, making it laborious to tune the parameter for different applications. We introduce a new metric that measures the informativeness of objects to be classified. When applied as a query-based distance metric to mea...

The K-Nearest Neighbor (KNN) join is an expensive but important operation in many data mining algorithms. Several recent applications need to perform KNN join for high dimensional sparse data. Unfortunately, all existing KNN join algorithms are designed for low dimensional data. To fulfill this void, we investigate the KNN join problem for high dimensional sparse data. In this paper, we propose...

The relationship between charge transport, defects and ferroelectric response is established for K0.5Na0.5NbO3 (KNN) and Mn-doped KNN ceramics. At room temperature the conduction in KNN is associated with hole transport and can be suppressed by Mn doping. Because of that a less leaky ferroelectric hysteresis loop is obtained for Mn-doped KNN. At high temperatures the conduction is dominated by ...

Associative classification usually generates a large set of rules. Therefore, it is inevitable that an instance matches several rules which classes are conflicted. In this paper, a new framework called Associative Classification with KNN (AC-KNN) is proposed, which uses an improved KNN algorithm to address rule conflicts. Traditional K-Nearest Neighbor (KNN) is low efficient due to its calculat...

Continuous K nearest neighbor queries (C-KNN) on moving objects retrieve the K nearest neighbors of all points along a query trajectory. In existing methods, the cost of retrieving the exact C-KNN data set is expensive, particularly in highly dynamic spatio-temporal applications. The cost includes the location updates of the moving objects when the velocities change over time and the number of ...

The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc. Its simplicity is its main advantage, but the disadvantages can’t be ignored even. The memory requirement and computation complexity also matter. Many techniques are developed to overcome these limitations. NN techniques are broadly...

In this paper, a new lead-free piezoelectric (K,Na)NbO3 (KNN) film is presented as a promising, environment-friendly alternative to the conventional piezoelectric thin film materials like PZT, etc. with regard to applying into piezo-MEMS devices in general and micro-energy-harvesting devices in particular. The KNN films deposited by the RF magnetron sputtering deposition system were revealed ex...

The paper contains the comparison between several class prediction methods (the K-Nearest Neighbour (KNN) algorithms and some variations of it) for classification of tumours using gene expression data. The KNN is a traditional classifier that uses a set of attributes for class prediction. Also are considered, the cases when these attributes (for KNN algorithm) are un-weighted (i.e. they all hav...

The standard Gaussian process (GP) regression is often intractable when a data set is large or spatially nonstationary. In this paper, we address these challenging data properties by designing a novel K nearest neighbor based Kalman filter Gaussian process (KNN-KFGP) regression. Based on a state space model established by the KNN driven data grouping, our KNN-KFGP recursively filters out the la...

The problem of k-nearest neighbors (kNN) search is to find nearest k neighbors from a given data set for a query point. To speed up the finding process of nearest k neighbors, many fast kNN search algorithms were proposed. The performance of fast kNN search algorithms is highly influenced by the number of dimensions, number of data points, and data distribution of a data set. In the extreme cas...