Nearest Neighbor Classification for High-Speed Big Data Streams using Spark

Big Data Classification using Fuzzy K-Nearest Neighbor

Because of the massive increase in the size of the data it becomes troublesome to perform effective analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of dat...

An Adaptive Nearest Neighbor Classification Algorithm for Data Streams

In this paper, we propose an incremental classification algorithm which uses a multi-resolution data representation to find adaptive nearest neighbors of a test point. The algorithm achieves excellent performance by using small classifier ensembles where approximation error bounds are guaranteed for each ensemble size. The very low update cost of our incremental classifier makes it highly suita...

K-Nearest Neighbor Classification on Spatial Data Streams

Classification of spatial data has become important due to the fact that there are huge volumes of spatial data now available holding a wealth of valuable information. In this paper we consider the classification of spatial data streams, where the training dataset changes often. New training data arrive continuously and are added to the training set. For these types of data streams, building a ...

K-Nearest Neighbor Classification Using Anatomized Data

This paper analyzes k nearest neighbor classification with training data anonymized using anatomy. Anatomy preserves all data values, but introduces uncertainty in the mapping between identifying and sensitive values. We first study the theoretical effect of the anatomized training data on the k nearest neighbor error rate bounds, nearest neighbor convergence rate, and Bayesian error. We then v...

Reverse Nearest Neighbor Aggregates Over Data Streams