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, ...

Description The input for clustering is a "sparse" distance matrix estimated by usearch -calc_distmx, which only stores a subset of distances, omitting pairs with low identities as determined by the ...

Graph clustering is a task that classifies vertices in a graph into many clusters according to their correlations. It is now widely used in many fields such as machine learning and so on, and the size ...

Learn how to overcome the main issues of spectral clustering, such as data preprocessing, cluster selection, eigenproblem solving, and complex data structures.

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 ...

Learn how to use non-negative matrix factorization (NMF) for clustering high-dimensional data, and what are its benefits and drawbacks.

In reference to the drawbacks of the CC algorithm (such as interference of random numbers in the greedy strategy; ignoring overlapping bi-clusters), we design an improved adaptive bi-clustering ...

We also show that two known methods for subspace clustering can be derived from the CUR decomposition. An algorithm based on the theoretical construction of similarity matrices is presented, and ...