We address the problem of k Nearest Neighbor (kNN) search in networks, according to a random walk proximity measure called Effective Importance. Our approach retrieves the exact top neighbors at query time without relying on off-line indexing or summaries of the entire network. This makes it suitable for very large dynamic networks, as well as for composite network overlays mixed at query time....

DEFINITION Given a set of n points and a query point, q, the nearest-neighbor problem is concerned with finding the point closest to the query point. Figure 1 shows an example of the nearest neighbor problem. On the left side is a set of n = 10 points in a two-dimensional space with a query point, q. The right shows the problem solution, s. Figure 1: An example of a nearest-neighbor problem dom...

Glaucoma according to the W.H.O is one of major causes blindness worldwide. Due its complexity and silent nature early detection this disease makes it hard detect. There have been several techniques over years for classification which shown significant improvement past decade or two. Some many models are SVM (support vector machine), KNN (K- Nearest Neighbors), Decision tree, Logistic Regressio...

Cachaça is a type of distilled drink from sugarcane with great economic importance. Its classification includes three types: aged, premium and extra premium. These three classifications are related to the aging time of the drink in wooden casks. Besides the aging time, it is important to know what the wood used in the barrel storage in order the properties of each drink are properly informed co...

drought forecasting in khash city by using neural network model hossein negaresh associate professor of geography and environmental planningfaculty, university of sistan & baluchestan mohsen armesh holding master degree in climatology in environmental planning extended abstract 1- introduction drought is condition of lack of rainfall and increase in temperature occurring in any climatic condit...

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 ...

Assessing the Probability of Forest Fire Occurring using Dong Model, Artificial Neural Network and K Nearest Neighbors in Babolrood Basin, Mazandaran

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...