The term "clustering model" is correct and usable in written English.
You can use it when referring to a type of algorithm or data mining technique used to group sets of objects or data records into clusters. For example, "We used a clustering model to uncover hidden patterns in the data."
An analytical clustering model with one-hop distance and clustering angle is given.
To address this issue, this paper proposes a robust subspace clustering model by embedding the Mixture of Gaussians (MoG) noise modeling strategy into the low-rank representation (LRR) subspace clustering model.
What we need is some kind of clustering model that will give us, basically, something like a taxonomic tree.
In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults.
In the identification of the TS fuzzy model, a hyperplane prototype fuzzy clustering model is proposed, which obtain the rules.
This paper introduces a systematic approach to reduce this discrepancy using data clustering, model selection, and cluster identification techniques.
Our system benefits from a fuzzy clustering model for fuzzy data to facilitate case retrieval and reduce the time complexity.
This 'Joint' ranking method coupled with an unsupervised topic clustering model is shown to have the potential to discover topics of interest or concern to a local community.
A large set of data from various published studies has been used here to test the suitability and need for a clustering model based reliability assessment.
We derive a necessary condition for the extremum point of the fuzzy minimax clustering model, and then design an iterative algorithm for solving the extremum point.
Clustering tends to reduce the difference between these exponents to less than their simple integer values, most markedly for the simple clustering model.
