We study the problem of automatically identifying“hotspots” on the real-time web. Concretely, we propose to identify highly-dynamic ad-hoc collections of users – what we ref...
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
Spectral data often have a large number of highly-correlated features, making feature selection both necessary and uneasy. A methodology combining hierarchical constrained clusteri...
This paper presents a Distributed Efficient Clustering Approach (DECA) for mobility-resistant and energy-efficient clustering in multi-hop wireless networks. The clusterheads cover...
Clustering is an important technique for understanding and analysis of large multi-dimensional datasets in many scientific applications. Most of clustering research to date has be...