Adaptive kernel fuzzy clustering for missing data

Many machine learning procedures, including clustering analysis are often affected by missing values. This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive distances (VKFCM-K-LP) under three types of strateg...

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Autores principales:	Anny K. G. Rodrigues, Raydonal Ospina, Marcelo R. P. Ferreira
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/566a041732bf443083188d6298371576
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spelling	oai:doaj.org-article:566a041732bf443083188d62983715762021-11-25T06:10:58ZAdaptive kernel fuzzy clustering for missing data1932-6203https://doaj.org/article/566a041732bf443083188d62983715762021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589222/?tool=EBIhttps://doaj.org/toc/1932-6203Many machine learning procedures, including clustering analysis are often affected by missing values. This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive distances (VKFCM-K-LP) under three types of strategies to deal with missing data. The first strategy, called Whole Data Strategy (WDS), performs clustering only on the complete part of the dataset, i.e. it discards all instances with missing data. The second approach uses the Partial Distance Strategy (PDS), in which partial distances are computed among all available resources and then re-scaled by the reciprocal of the proportion of observed values. The third technique, called Optimal Completion Strategy (OCS), computes missing values iteratively as auxiliary variables in the optimization of a suitable objective function. The clustering results were evaluated according to different metrics. The best performance of the clustering algorithm was achieved under the PDS and OCS strategies. Under the OCS approach, new datasets were derive and the missing values were estimated dynamically in the optimization process. The results of clustering under the OCS strategy also presented a superior performance when compared to the resulting clusters obtained by applying the VKFCM-K-LP algorithm on a version where missing values are previously imputed by the mean or the median of the observed values.Anny K. G. RodriguesRaydonal OspinaMarcelo R. P. FerreiraPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anny K. G. Rodrigues Raydonal Ospina Marcelo R. P. Ferreira Adaptive kernel fuzzy clustering for missing data
description	Many machine learning procedures, including clustering analysis are often affected by missing values. This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive distances (VKFCM-K-LP) under three types of strategies to deal with missing data. The first strategy, called Whole Data Strategy (WDS), performs clustering only on the complete part of the dataset, i.e. it discards all instances with missing data. The second approach uses the Partial Distance Strategy (PDS), in which partial distances are computed among all available resources and then re-scaled by the reciprocal of the proportion of observed values. The third technique, called Optimal Completion Strategy (OCS), computes missing values iteratively as auxiliary variables in the optimization of a suitable objective function. The clustering results were evaluated according to different metrics. The best performance of the clustering algorithm was achieved under the PDS and OCS strategies. Under the OCS approach, new datasets were derive and the missing values were estimated dynamically in the optimization process. The results of clustering under the OCS strategy also presented a superior performance when compared to the resulting clusters obtained by applying the VKFCM-K-LP algorithm on a version where missing values are previously imputed by the mean or the median of the observed values.
format	article
author	Anny K. G. Rodrigues Raydonal Ospina Marcelo R. P. Ferreira
author_facet	Anny K. G. Rodrigues Raydonal Ospina Marcelo R. P. Ferreira
author_sort	Anny K. G. Rodrigues
title	Adaptive kernel fuzzy clustering for missing data
title_short	Adaptive kernel fuzzy clustering for missing data
title_full	Adaptive kernel fuzzy clustering for missing data
title_fullStr	Adaptive kernel fuzzy clustering for missing data
title_full_unstemmed	Adaptive kernel fuzzy clustering for missing data
title_sort	adaptive kernel fuzzy clustering for missing data
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/566a041732bf443083188d6298371576
work_keys_str_mv	AT annykgrodrigues adaptivekernelfuzzyclusteringformissingdata AT raydonalospina adaptivekernelfuzzyclusteringformissingdata AT marcelorpferreira adaptivekernelfuzzyclusteringformissingdata
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Adaptive kernel fuzzy clustering for missing data

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