Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a...
The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean shift algorithm with heterogeneous node weights that come f...
In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the c...
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
Data grids deal with a huge amount of data regularly. It is a fundamental challenge to ensure efficient accesses to such widely distributed data sets. Creating replicas to a suita...