Machine learning (ML) models in production pipelines are frequently retrained on the latest
partitions of large, continually-growing datasets. Due to engineering bugs, partitions in such …

We introduce the concept of an-semimetric that satisfies the same axioms as a topological
metric, except for an arbitrarily small allowance to violate the triangle inequality. Under this …

Graph clustering methods are popular due to their ability to discover clusters with arbitrary
shapes. However, with the emergence of large-scale datasets, the efficiency of graph …

Mining data containing density-based clusters is well-established and widespread but faces
problems when it comes to systematic and reproducible comparison and evaluation …

Efficient clustering algorithms, such as k-Means, are often used in practice because they
scale well for large datasets. However, they are only able to detect simple data …

Machine learning (ML) models in production pipelines are frequently retrained on the latest
partitions of large, continually-growing datasets. Due to engineering bugs, partitions in such …

Traditional spectral clustering methods struggle with scalability and robustness in large
datasets due to their reliance on similarity matrices and eigenvalue decomposition. We …

<? show [AQ ID= GQ2 POS=-12pt]?><? show [AQ ID= GQ5 POS= 24pt]?> Segmenting
anterior and posterior cruciate ligaments (ACL/PCL) presents challenges in medical imaging …

Hierarchical clustering is a powerful tool for exploratory data analysis, organizing data into a
tree of clusterings from which a partition can be chosen. This paper generalizes these ideas …

Graph Theory has proven to be a universal language for describing modern complex
systems. The elegant theoretical framework of graphs drew the researchers' attention over …