Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …

Clustering is central to many data-driven bioinformatics research and serves a powerful
computational method. In particular, clustering helps at analyzing unstructured and high …

Finite mixture models are being used increasingly to model a wide variety of random
phenomena for clustering, classification and density estimation. mclust is a powerful and …

Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic
diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue …

Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

Density based clustering algorithms (DBCLAs) rely on the notion of density to identify
clusters of arbitrary shapes, sizes with varying densities. Existing surveys on DBCLAs cover …

An integrated framework for density-based cluster analysis, outlier detection, and data
visualization is introduced in this article. The main module consists of an algorithm to …

We propose a theoretically and practically improved density-based, hierarchical clustering
method, providing a clustering hierarchy from which a simplified tree of significant clusters …

Medulloblastoma is a highly malignant paediatric brain tumour currently treated with a
combination of surgery, radiation and chemotherapy, posing a considerable burden of …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …