This python package implements k-medoids clustering with PAM and variants of clustering by direct optimization of the (Medoid) Silhouette. It can be used with arbitrary dissimilarites, as it requires ...

Abstract: Spectral clustering is a leading clustering method. Two of its major shortcomings are the disjoint optimization process and the limited representation capacity. To address these issues, we ...

Compared to other clustering techniques, DBSCAN does not require you to explicitly specify how many data clusters to use, explains Dr. James McCaffrey of Microsoft Research in this full-code, ...

Identifying different types of cells in scRNA-seq data is a critical task in single-cell data analysis. In this paper, we propose a method called ProgClust for the decomposition of cell populations ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. We propose a scheme for the automatic separation (i.e., clustering) of data sets ...

The flow field obtained from streamline simulation reflects key properties of the reservoir, such as the distribution of the remaining oil and the location of channels. However, in the ...

NMFk is a module of the SmartTensors ML framework (smarttensors.com). NMFk is a novel unsupervised machine learning methodology that allows for the automatic identification of the optimal number of ...

Abstract: The underdetermined blind source separation (UBSS) has been considered to be a novel signal processing technique, which can separate the fault source signals from their mixtures. The mixing ...

Multilayer network clustering is used in such diverse areas as optimal islanding of critical infrastructures, analysis of trade agreements, and monitoring ecological interaction patterns. We propose a ...

Since the great financial crisis of 2008, many studies have pointed out that even in the portfolio where the asset allocation is sufficiently diversified, it is still possible that risk allocation is ...