Haghir Chehreghani, Morteza http://orcid.org/0000-0002-2912-7422
Funding for this research was provided by:
Knut och Alice Wallenbergs Stiftelse
Chalmers University of Technology
Article History
Received: 29 November 2021
Revised: 3 February 2022
Accepted: 9 May 2022
First Online: 22 June 2022
Declarations
:
: Not Applicable. No conflict of interest occurs.
: This research is mainly focused on conceptual and methodological developments in unsupervised learning and clustering. Clustering is usually used for data management and exploratory data analytics. Thus, this contribution provides methods to further understand, explore and explain the data and obtain deeper insights. Such possibilities can be used for example to understand gender-specific features, data irregularities, private and sensitive information and explainability aspects. On the other hand, the use of clustering for data management and summarization provides a systematic way to compress the data to yield more efficient data precessing in terms of energy and memory usage. This, itself, can be helpful for better environmental conditions. These properties are critical when dealing with large amount of data, in particular for environment friendly solutions. Finally, we would like to emphasize that in this work the experimental studies use the datasets which do not contain any private and sensitive information.
: Not Applicable. There is no human study in this research.
: Not Applicable. No human study is performed in this research. There is no sensitive information.
: The code will be available through the author’s home page and will be maintained there with a reference to this publication.