The traditional Gaussian process (GP) regression is often deteriorated when the data set is large-scale and/or non-stationary. To address these challenging data properties, we propose a K-Nearest-Neighbor-based Kalman filter for Gaussian process regression (KNN-KFGP). Firstly, we design a test-inputdriven KNN mechanism to group the training set into a number of small collections. Secondly, we u...

k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for every object in another dataset R, is a primitive operation widely adopted by many data mining applications. As a combination of the k nearest neighbor query and the join operation, kNN join is an expensive operation. Given the increasing volume of data, it is difficult to perform a kNN join on a centr...

1&2 Department of Electronics and Communication 1&2 Punjabi University 1&2 Rajpura Road, Patiala 1&2 INDIA Abstract:Noisy Electrocardiogram (ECG) signal can mask some of the important features of the original ECG signal. Therefore, it is necessary to remove the noise for proper analysis of the ECG signal. In this paper, the use of Kohonen Neural Network (KNN) for automatically identifying the c...

We present here the results for a comparative study of some techniques, available in the literature, related to the relevance feedback mechanism in the case of a short-term learning. Only one method among those considered here is belonging to the data mining field which is the K-nearest neighbors algorithm (KNN) while the rest of the methods is related purely to the information retrieval field ...

Defect severity assessment is highly essential for the software practitioners so that they can focus their attention and resources on the defects having a higher priority than the other defects. This would directly impact resource allocation and planning of subsequent defect fixing activities. In this paper, we intend to predict a model which will be used to assign a severity level to each of t...

Data mining has been flourishing in the information-based world. In data mining, the DTW-kNN framework is widely applied for classification in miscellaneous application domains. Most of the studies in the DTW-kNN framework focus on accuracy and speedup. However, with increasingly emphasis on applications of mobile and embedded systems, energy efficiency becomes an urgent consideration in data m...

The k-Nearest Neighbor (kNN) method of forest attribute estimation and mapping has become an integral part of national forest inventory methods in Finland in the last decade. This success of kNN method in facilitating multi-source inventory has encouraged trials of the method in the Great Lakes Region of the United States. Here we present results from applying the method to Landsat TM and ETM+ ...

data types Storage engine Query processor SQL Language Continuous time-based Sliding Window Queries Continuous Predicate-based Window Queries Moving Queries Stream data types Stream of Moving Objects/Queries Stream_Scan Operator W-Expire Operator Negative Tuples INSIDE Operator kNN Operator WINDOW window_clause kNN knn_clause PLACE NILE INSIDE inside_clause

Classification of spatial data has become important due to the fact that there are huge volumes of spatial data now available holding a wealth of valuable information. In this paper we consider the classification of spatial data streams, where the training dataset changes often. New training data arrive continuously and are added to the training set. For these types of data streams, building a ...