Abstract: The paper considers the problem of density estimation and clustering in distributed sensor networks. It is assumed that each node in the network senses an environment that can be described ...

Automated apple harvesting is hindered by clustered fruits, varying illumination, and inconsistent depth perception in complex orchard environments. While deep learning models such as Faster R-CNN and ...

Fits MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>, i.e. fits finite Gaussian mixture of experts models with gating and/or ...

Clustering is commonly used in single-cell RNA-sequencing (scRNA-seq) pipelines to characterize cellular heterogeneity. However, current methods face two main limitations. First, they require ...

ABSTRACT: This paper is concerned about studying modeling-based methods in cluster analysis to classify data elements into clusters and thus dealing with time series in view of this classification to ...

Abstract: Though very popular, it is well known that the Expectation-Maximisation (EM) algorithm for the Gaussian mixture model performs poorly for non-Gaussian distributions or in the presence of ...

Data clustering is the process of grouping data items so that similar items are placed in the same cluster. There are several different clustering techniques, and each technique has many variations.

Center for Computational Mathematics, Flatiron Institute, New York, New York 10010, United States Center for Computational Biology, Flatiron Institute, New York, New York 10010, United States Article ...