Dynamic Clustering

Dynamic Based Contour Clustering

II Acknowledgements III List of Figures VI

Dynamic Spectral Clustering

Clustering is a powerful tool for data classification; however, its application has been limited to analysis of static snapshots of data which may be time-evolving. This work presents a clustering algorithm that employs a fixed time interval and a time-aggregated similarity measure to determine classification. The fixed time interval and a weighting parameter are tuned to the system’s dynamics;...

Dynamic Tensor Clustering

Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor. Also there is often a gap between statistical guarantee and computational efficiency for existing tensor clustering solutions. In this article, we aim to bridge this gap by proposing a ne...

Isotropic Dynamic Hierarchical Clustering

We face a business need of discovering a pattern in locations of a great number of points in a high-dimensional space. We assume that there should be a certain structure, so that in some locations the points are close while in other locations the points are more dispersed. Our goal is to group the close points together. The process of grouping close objects is known under the name of clustering...

Dynamic Neural Clustering