Sentence examples for machine learning clustering from inspiring English sources

Data analysis is performed by applying statistical techniques, data mining, machine learning, clustering, and pattern recognition to achieve a better understanding of the behavior of users.

It combines machine learning clustering and stochastic theory to predict the number of the upcoming VM requests along with required resources associated with each request.

As was mentioned earlier, the analysis applications that are run on the data may apply statistical techniques, data mining, machine learning, clustering and classification, and pattern recognition functions.

Following up on Müller et al., Newman and Cooper [9] used their AutoSOME machine learning (clustering) approach to generate the PluriUp gene set as a cluster of 3421 genes "upregulated in pluripotent stem cells", and the PluriPlus network as the subset of 1165 PluriUp genes that are interconnected by verified protein-protein interactions from the Human Protein Reference Database (HPRD, [10]).

Fourthly, we developed a machine learning clustering algorithm and have implemented it using C++ parallel programming for fast computation.

A Self-Organizing Map (SOM) is another unsupervised machine learning clustering technique that has been used in two recent publications to analyze a large number of ChIP-seq (and DNase-seq) datasets using maps with potentially at least a thousand such micro-states [ 39, 40].

This data sharpens the ever-present edge of machine learning clusters that know you better than yourself.

Different types of machine learning algorithms, clustering and classifying algorithms, and evaluating their applicability to modern intrusion detection techniques are explained.

The resulting algorithms are highly efficient and robust, and can be used in simulations ranging from minimal partition problems in Euclidean space to semi-supervised machine learning via clustering on graphs.

Since there has been no systematic summary on this topic, in this paper, we review the main-stream graph construction/learning methods involved in both general machine learning algorithms (including semi-supervised learning, clustering, manifold learning, and spectral kernel learning, etc).

The paper presents two new concepts: First, a set of clustering algorithms that model the supply-demand characterization of four different substations clusters, and second, a semi-supervised machine learning and clustering technique are developed to optimize the losses and automate the process of identifying loss factors contributing to the total loss.