Clustering as a research area investigates methods for partitioning data into groups (clusters) such that objects within a cluster are more similar to each other than to those in other clusters, without reliance on labeled examples. It encompasses algorithmic development (e.g., centroid-based, density-based, model-based, graph and spectral methods), similarity and distance metric design, scalability for high-dimensional and large-scale data, and theoretical analysis of cluster validity and stability. Research also addresses probabilistic and Bayesian formulations, optimization criteria, robustness to noise and outliers, and domain-specific adaptations in fields such as bioinformatics, information retrieval, computer vision, and social network analysis.
Avoiding the formation of unwanted clusters of similar elements when dividing data into groups is of great importance for the analysis of medical data. Psychologists and computer scientists from Heinrich Heine University ...
Molecular & Computational biology
Aug 18, 2025
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A research team led by Prof. Wang Huiyuan from the University of Science and Technology of China (USTC) has made a groundbreaking discovery, identifying for the first time an exceptionally strong clustering pattern in diffuse ...
Astronomy
May 22, 2025
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When I first started working with integral field spectroscopic (IFU) data, I was struck by how much complexity was being averaged out or masked by traditional processing techniques. Most segmentation methods in astronomy—especially ...
Through his research at Caltech, a local high school student revealed 1.5 million previously unknown objects in space, broadened the potential of a NASA mission, and published a single-author paper.
Astronomy
Apr 11, 2025
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