The class of k Nearest Neighbor (kNN) queries in spatial networks is extensively studied in the context of numerous applications. In this paper, for the first time we study a generalized form of this problem, called the Time-Dependent k Nearest Neighbor problem (TD-kNN) with which edge-weights are time variable. All existing approaches for kNN search assume that the weight (e.g., travel-time) o...

Currently, there has been an increasing development in the area of location-based service. An important type of query in this area is k nearest neighbor (kNN) query, which retrieves the top k nearest neighbors based on the user's position. Although a wide spectrum of work has been conducted on this query type, most of these studies focus on the ideal Euclidean plane without obstacles considered...

 Abstract—KNN classification finds k nearest neighbors of a query in training data and then predicts the class of the query as the most frequent one occurring in the neighbors. This is a typical method based on the majority rule. Although majority-rule based methods have widely and successfully been used in real applications, they can be unsuitable to the learning setting of skewed class distr...

ML-kNN is a well-known algorithm for multi-label classification. Although effective in some cases, ML-kNN has some defect due to the fact that it is a binary relevance classifier which only considers one label every time. In this paper, we present a new method for multi-label classification, which is based on lazy learning approaches to classify an unseen instance on the basis of its k nearest ...

Classification of spatial data streams is crucial, since the training dataset changes often. Building a new classifier each time can be very costly with most techniques. In this situation, k-nearest neighbor (KNN) classification is a very good choice, since no residual classifier needs to be built ahead of time. KNN is extremely simple to implement and lends itself to a wide variety of variatio...

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...

K-Nearest Neighbor (KNN) is highly efficient classification algorithm due to its key features like: very easy to use, requires low training time, robust to noisy training data, easy to implement. However, it also has some shortcomings like high computational complexity, large memory requirement for large training datasets, curse of dimensionality and equal weights given to all attributes. Many ...

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...

Data Mining has great scope in the field of medicine. In this article we introduced one new fuzzy approach for prediction of hepatitis disease. Many researchers have proposed the use of K-nearest neighbor (KNN) for diabetes disease prediction. Some have proposed a different approach by using K-means clustering for reprocessing and then using KNN for classification. In our approach Naive Bayes c...