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...

(K(x),Na(1-x))NbO(3) (KNN) thin films were deposited on (001)SrRuO(3)/(001)Pt/(001)MgO substrates by RF-magnetron sputtering, and their piezoelectric properties were investigated. The x-ray diffraction measurements indicated that the KNN thin films were epitaxially grown with the c-axis orientation in the perovskite tetragonal system. The lattice constant of the c-axis increased with increasing...

Accurate probability-based ranking of instances is crucial in many real-world data mining applications. KNN (k-nearest neighbor) [1] has been intensively studied as an effective classification model in decades. However, its performance in ranking is unknown. In this paper, we conduct a systematic study on the ranking performance of KNN. At first, we compare KNN and KNNDW (KNN with distance weig...

For classification of time series, the simple 1-nearest neighbor (1NN) classifier in combination with an elastic distance measure such as Dynamic Time Warping (DTW) distance is considered superior in terms of classification accuracy to many other more elaborate methods, including k-nearest neighbor (kNN) with neighborhood size k > 1. In this paper we revisit this apparently peculiar relationshi...