Kernel density estimators are the basic tools for density estimation in non-parametric statistics.  The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. In this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

We study continuous K nearest neighbor queries over moving objects. LUCK stands for Lazy Update algorithm for processing Continuous K-nearest neighbor queries.

K-nearest neighbor (kNN) search is an important problem in data mining and knowledge discovery. Inspired by the huge success of tree-based methodology ensemble methods over last decades, we propose a new method for kNN search, random projection forests (rpForests). rpForests finds nearest neighbors combining multiple kNN-sensitive trees with each constructed recursively through series projectio...

This paper proposes a new --rough nearest-neighbor (NN ) approach based on the fuzzy-rough sets theory. This approach is more suitable to be used under partially exposed and unbalanced data set compared with crisp NN and fuzzy NN approach. Then the new method is applied to China listed company financial distress prediction, a typical classification task under partially exposed and unbalanced le...

Detection and segmentation of Brain tumor is very important because it provides anatomical information of normal and abnormal tissues which helps in treatment planning and patient follow-up. There are number of techniques for image segmentation. Proposed research work uses ANFIS (Artificial Neural Network Fuzzy Inference System) for image classification and then compares the results with FCM (F...

The k-nearest neighbor rule is one of the simplest and most attractive pattern classification algorithms. It can be interpreted as an empirical Bayes classifier based on the estimated a posteriori probabilities from the k nearest neighbors. The performance of the k-nearest neighbor rule relies on the locally constant a posteriori probability assumption. This assumption, however, becomes problem...