are clustered using vs PCA-based clustering

These two phrases are not directly comparable as they serve different purposes. 'Are clustered using' is a general phrase used to describe the process of grouping data points, while 'PCA-based clustering' specifically refers to clustering techniques that utilize Principal Component Analysis. Depending on the context, one might be more appropriate than the other.

Last updated: March 17, 2024 • 729 views

are clustered using

This phrase is correct and commonly used in English to describe the process of grouping data points based on certain criteria.

This phrase is used to indicate that data points are grouped together using a specific clustering method or algorithm.

Examples:

The data points are clustered using the K-means algorithm.
The customers are clustered using demographic information.
The students are clustered using their test scores.

Show examples from the web [+]

In the second step, the preclusters are clustered using the hierarchical clustering algorithm. You can specify the number of clusters you want or let the algorithm ...
... in Oracle database environments. Oracle Corporation includes RAC with the Standard Edition, provided the nodes are clustered using Oracle Clusterware.
Feb 20, 2007 ... However, when molecules are clustered using fingerprints, it may be difficult to decipher the structural commonalities which are present. Here ...
When such jobs are clustered using horizontal clustering, the benefits of job clustering may be lost if all smaller jobs get clustered together, while the larger jobs ...

Alternatives:

are grouped using
are classified using
are organized using
are sorted using
are categorized using

PCA-based clustering

This phrase is correct and commonly used in the context of data analysis and machine learning to refer to clustering techniques that leverage Principal Component Analysis (PCA).

This phrase specifically denotes clustering methods that utilize PCA as part of the clustering process.

Examples:

PCA-based clustering helps in reducing the dimensionality of the data before clustering.
The researchers applied PCA-based clustering to identify patterns in the dataset.
PCA-based clustering is effective for high-dimensional data.

Show examples from the web [+]

... adapted for population genetics analysis. ELKI – includes PCA for projection, including robust variants of PCA, as well as PCA-based clustering algorithms.
Kernel PCA Based Clustering for Inducing. Features in Text Categorization. Zsolt Minier1 and Lehel Csató1 *. 1- Babes-Bolyai University - Department of ...
Kernel PCA Based Clustering for Inducing. Features in Text Categorization. Zsolt Minier. 1 and Lehel Csató. 1 *. 1- Babes-Bolyai University - Department of ...
Jan 10, 2014 ... Improving Scalability of Cloud Monitoring Through PCA-Based Clustering of Virtual Machines. Claudia CanaliAffiliated withDepartment of ...

Alternatives:

clustering with PCA
PCA-driven clustering
clustering based on PCA
PCA-enhanced clustering
clustering using principal component analysis

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are clustered using vs PCA-based clustering