The phrase "a clustering process" is correct and usable in written English.
It can be used in contexts related to data analysis, machine learning, or any situation where items are grouped based on similarities.
Example: "The algorithm employs a clustering process to categorize the data into distinct groups for better analysis."
Alternatives: "a grouping method" or "a categorization technique".
Inter-cluster separation which is widely used in classification algorithms, however, is rarely considered in a clustering process.
In this paper, we regard the feature selection as a clustering process with data decomposition technique and propose a novel feature selection method based on the non-negation matrix factorization (NMF).
These data are compatible with a clustering process of GM1 molecules, an effect that not only increases the Aβ peptide affinity, but also causes a pronounced Aβ peptide penetration deeper into the lipid membrane; all these factors are potentially involved in Aβ peptide aggregate formation due to an altered ganglioside metabolism found in AD patients.
The global motion induced by camera movement is determined by a clustering process that consists of the following steps.
If the cluster heads have enough energy and the clustering timer expires, then there is a clustering process in the whole network, which will waste energy consumption.
Among all distance measures, a clustering process was usually carried out through the Euclidean distance measure [7], but many times it failed to offer good results.
A discriminative clustering process is used to discover a common latent feature space that is domain invariant while simultaneously learning the final target classifier.
A typical clustering process involves feature selection, selection of a similarity measure, grouping of data, and assessment of the output.
To achieve this, we propose a weighted clustering process in the joint input output data space.
Combination of a spatial clustering process reveals the most suitable areas for rural buildings siting with their landscapes.
The approach includes a novel clustering process where initial incomplete models developed through knowledge engineering are used to detect action clusters which represent activities and aggregate new actions.
