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...

K-Nearest Neighbor (KNN) is one of the most popular algorithms for data classification. Many researchers have found that the KNN algorithm accomplishes very good performance in their experiments on different datasets. The traditional KNN text classification algorithm has limitations: calculation complexity, the performance is solely dependent on the training set, and so on. To overcome these li...

K-Nearest Neighbor (KNN) is one of the most popular algorithms for data classification. Many researchers have found that the KNN algorithm accomplishes very good performance in their experiments on different datasets. The traditional KNN text classification algorithm has limitations: calculation complexity, the performance is solely dependent on the training set, and so on. To overcome these li...

recently several 1,3,4-oxadiazole derivatives were identified as potentially active antimycobacterial agents. various 5-aryl-2-thio-1,3,4-oxadiazoles have been reported having good antimycobacterial activity against mycobacterium tuberculosis h37rv (atcc 27294). in this paper we report 3d qsar studies for the 41 molecules of 1,3,4-oxadiazoles by using k-nearest neighbor molecular field analysis...

The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc. Its simplicity is its main advantage, but the disadvantages can’t be ignored even. The memory requirement and computation complexity also matter. Many techniques are developed to overcome these limitations. NN techniques are broadly...

Lazy classifiers store all of the training samples and do not build a classifier until a new sample needs to be classified. It differs from eager classifiers, such as decision tree induction, which build a general model (such as a decision tree) before receiving new samples. K-nearest neighbor (KNN) classification is a typical lazy classifier. Given a set of training data, a knearest neighbor c...