We describe a general-purpose method for the accurate and robust interpretation of a data set of p-dimensional points by several deformable prototypes. This method is based on the ...
Data availability, collection and storage have increased dramatically in recent years, raising new technological and algorithmic challenges for database design and data management...
Central and subspace clustering methods are at the core of many segmentation problems in computer vision. However, both methods fail to give the correct segmentation in many pract...
This paper investigates the applicability of distributed clustering technique, called RACHET [1], to organize large sets of distributed text data. Although the authors of RACHET c...
Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity meas...