The enormous growth and usage of social networks offer positive ways to any business by sharing the emotions, feelings and experiences. Web users are benefited with valuable online reviews. To utilize the reviews effectively, researchers are working on necessary methods and ideas such as classification of positive and negative sense of reviews, ranking the facet in the reviews to make the effec...

We present Stochastic Neighbor Compression (SNC), an algorithm to compress a dataset for the purpose of k-nearest neighbor (kNN) classification. Given training data, SNC learns a much smaller synthetic data set, that minimizes the stochastic 1-nearest neighbor classification error on the training data. This approach has several appealing properties: due to its small size, the compressed set spe...

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 ...