A novel supervised cluster adjustment method using a fast exact nearest neighbor search algorithm

Abstract

Supervised clustering is a new research area that aims to improve unsupervised clustering algorithms exploiting supervised information. Today, there are several clustering algorithms, but the effective supervised cluster adjustment method which is able to adjust the resulting clusters, regardless of applied clustering algorithm has not been presented yet. In this paper, we propose a new supervised cluster adjustment method which can be applied to any clustering algorithm. Since the adjustment method is based on finding the nearest neighbors, a novel exact nearest neighbor search algorithm is also introduced which is significantly faster than the classic one. Several datasets and clustering evaluation metrics are employed to examine the effectiveness of the proposed cluster adjustment method and the proposed fast exact nearest neighbor algorithm comprehensively. The experimental results show that the proposed algorithms are significantly effective in improving clusters and accelerating nearest neighbor searches.