Collaborative Filtering (CF) is the most popular choice when implementing personalized recommender systems. A classical approach to CF is based on K-nearest-neighborhood (KNN) model, where the precondition for making recommendations is the KNN construction for involved entities. However, when building KNN sets, there exits the dilemma to decide the value of K --a small value will lead to poor r...

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 ...