MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generation

Chandrani Kumari; Rahul Siddharthan

doi:10.1371/journal.pone.0302271

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Open Access

Peer-reviewed

Research Article

MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generation

Chandrani Kumari,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations The Institute of Mathematical Sciences, Chennai, India, Homi Bhabha National Institute, Mumbai, India

https://orcid.org/0000-0002-7107-7334

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Rahul Siddharthan

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: rsidd@imsc.res.in

Affiliations The Institute of Mathematical Sciences, Chennai, India, Homi Bhabha National Institute, Mumbai, India

https://orcid.org/0000-0002-2233-0954

⨯

MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generation

Chandrani Kumari,
Rahul Siddharthan

Published: April 17, 2024
https://doi.org/10.1371/journal.pone.0302271

About the Authors

Chandrani Kumari

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations The Institute of Mathematical Sciences, Chennai, India, Homi Bhabha National Institute, Mumbai, India

https://orcid.org/0000-0002-7107-7334

Rahul Siddharthan

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: rsidd@imsc.res.in

Affiliations The Institute of Mathematical Sciences, Chennai, India, Homi Bhabha National Institute, Mumbai, India

https://orcid.org/0000-0002-2233-0954

Competing Interests

The authors have declared that no competing interests exist.

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Machine learning algorithms
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Normal distribution
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Clustering algorithms
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Sonar
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Deep learning
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Diabetes mellitus
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