Topic modeling algorithms are statistical methods that aim to discover the topics running through the text documents. Using topic models in machine learning and text mining is popular due to its applicability in inferring ...

This work considers probability models for partitions of a set of n elements using a predictive approach, i.e., models that are specified in terms of the conditional probability of either joining an already existing cluster ...

In this paper the Optimized Vector and Marginal Median Self-Organizing Map (OVMMSOM) was proposed as a new method of train Self-Organizing Maps (SOM). This variant is based on order statistics, Marginal Median SOM (MMSOM) ...

There are few studies using cluster models in the nuclei around the intermediate and heavy mass regions. The alpha-cluster model is based on the interaction between an alpha particle and a nucleus chosen as a core. This ...

Recurrent incidence of infant diarrhoea is studied, using daily data collected in Salvador, Brazil, from 754 children over 455 days. Aalen's additive intensity model is taken as the basis of the modelling strategy and a ...

This research created a new grouping alternative using machine learning tools such as K-means and agglomerative clustering models, based on financial information from 2016 to 2019 of 10,001 Colombian SMEs. From these ...

An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced in this article. The main module consists of an algorithm to compute hierarchical estimates of the level ...

We present a new method to quantify substructures in clusters of galaxies, based on the analysis of the intensity of structures. This analysis is done in a residual image that is the result of the subtraction of a surface ...