BALLKNN: An efficient and scalable KNN based on Euclidean similarity

Abstract

The K Nearest Neighbor (KNN) technique is very simple, highly efficient and effective in the field of text categorization, pattern recognition, object recognition etc. However, its efficiency is challenged by Big Data, which is characterized with volume, variety and velocity. All these characters require a new KNN algorithm with better time complexity, which is lower than O(n) at least Additionally, the characters witness that centralized variants executed in a single thread are no longer reasonable. In this paper, we propose an efficient and scalable strategy, which we called as BALLKNN, for KNN classifier. In BALLKNN, since computation of some samples are reduced by replacing complicated quadratic sum with simple subduction and comparison, time efficiency improvement is obtained; the disadvantage of dependency on global data structure is overcome with the help of temporary variable(s), BALLKNN can be implemented with multiple threads easily, and it scales well with the size of the dataset. Meanwhile, as all samples are handled, the classification accuracy is guaranteed different from traditional sample reduction methods. Furthermore, detailed theoretical analysis on relationship between dimensionality of input and time complexity is provided. Finally, extensive experiments in large real datasets, with tens or hundreds of thousands of records and up to 618 dimensions, have demonstrated the efficiency and scalability of our methods.