Clustering can be understood as a matrix decomposition problem, where a feature vector matrix is represented as a product of two matrices, a matrix of cluster centres and a matrix with sparse columns, ...

The 32-bit version of USEARCH cannot process large distance matrices due to memory limitations. This can be a significant bottleneck when working with large sequence datasets. To overcome this ...

A co-clustering algorithm for large sparse binary data matrices, based on a greedy technique and enriched with a local search strategy to escape poor local maxima, is proposed. The algorithm starts ...

Spectral algorithms are widely applied to data clustering problems, including finding communities or partitions in graphs and networks. We propose a way of encoding sparse data using a ...

Hierarchical clustering for sparse matrices with constraints on merging based on overlap and height thresholds. Efficient Union-Find data structure tailored to support the package's clustering methods ...

• A clustering algorithm based on the methodology of the Robust Shape Interaction Matrix of Ji et al ... Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of ...

Unlike some other clustering algorithms, such as k-means, ... This is a computationally expensive and numerically unstable task, especially for large and sparse matrices.

Bi-clusters can thus be seen as sub-matrices of a matrix representing features of elements. It should be noted that bi-clusters need not to be exclusive nor exhaustive (Xhafa et al., 2011).The ...