… Practical statistical data clustering algorithms require multiple data scans … clustering framework
requiring at most one scan of the database, and apply it to the Expectation-Maximization (…

… of the clustering algorithms are the K-means and the expectation maximization (EM) algorithm…
is applied to EM clusters and the K-means clustering method for quality assessment of red …

… We introduce the Expectation-Maximization binary Clustering (EMbC), a general … data
clustering. The EMbC is a variant of the Expectation-Maximization Clustering (EMC), a clustering …

… For instance, in gene expression clustering 5 , we are given … expression vectors into distinct
clusters of related genes. One … variables) associated with that gene's cluster. In this case, the …

… In this article, we extend the well-known expectationmaximization (EM) formulation for the …
of two different clustering methods, namely, centroid-based clustering and graph clustering, in …

… A more general model dedicated to temporal data clustering is proposed in this section.
In contrast to the penalized likelihood approach described in the previous section, which …

… Expectation Maximization (EM) is a widely employed mixture model-based data clustering
… requires huge computational efforts than other clustering algorithms. This paper presents an …

… This research will optimize the parameters k in the UN tax cluster using the … form of clustering
information by using the number of clusters k value optimization and the number of clusters …

… In this paper the Expectation-Maximization clustering algorithm is applied to determine
similar objects. This method generates fewer features with either a higher or the same accuracy …

… clustering algorithms considered in this study are K-means and ExpectationMaximization …
not to guarantee convergence while Expectation-Maximization’s quick convergence doesn’t …