{"title":"SSFCM-FWCW：基于特征-权值和聚类-权值学习的半监督模糊 C-Means 方法","authors":"Amin Golzari Oskouei , Negin Samadi , Jafar Tanha , Asgarali Bouyer","doi":"10.1016/j.simpa.2024.100678","DOIUrl":null,"url":null,"abstract":"<div><p>SSFCM-FWCW (Feature-Weight and Cluster-Weight based Semi-Supervised Fuzzy <em>C</em>-Means) is a soft clustering method. It incorporates supplementary label information to enhance the clustering quality. An adaptive local feature weighting technique is utilized to weight features based on their significance within specific clusters. Additionally, an adaptive weighting technique is applied to diminish the sensitivity to the initial center selection, effectively distinguishing between the effects of various clusters. The conjunction of label information and adaptive weighting results in an optimal fuzzy <em>c</em>-means clustering with an insight into the importance of individual features and clusters. An open-source Matlab implementation of SSFCM-FWCW is available.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"21 ","pages":"Article 100678"},"PeriodicalIF":1.3000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000666/pdfft?md5=ef848f90365139295625e2a7b7f9c617&pid=1-s2.0-S2665963824000666-main.pdf","citationCount":"0","resultStr":"{\"title\":\"SSFCM-FWCW: Semi-Supervised Fuzzy C-Means method based on Feature-Weight and Cluster-Weight learning\",\"authors\":\"Amin Golzari Oskouei , Negin Samadi , Jafar Tanha , Asgarali Bouyer\",\"doi\":\"10.1016/j.simpa.2024.100678\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>SSFCM-FWCW (Feature-Weight and Cluster-Weight based Semi-Supervised Fuzzy <em>C</em>-Means) is a soft clustering method. It incorporates supplementary label information to enhance the clustering quality. An adaptive local feature weighting technique is utilized to weight features based on their significance within specific clusters. Additionally, an adaptive weighting technique is applied to diminish the sensitivity to the initial center selection, effectively distinguishing between the effects of various clusters. The conjunction of label information and adaptive weighting results in an optimal fuzzy <em>c</em>-means clustering with an insight into the importance of individual features and clusters. An open-source Matlab implementation of SSFCM-FWCW is available.</p></div>\",\"PeriodicalId\":29771,\"journal\":{\"name\":\"Software Impacts\",\"volume\":\"21 \",\"pages\":\"Article 100678\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2665963824000666/pdfft?md5=ef848f90365139295625e2a7b7f9c617&pid=1-s2.0-S2665963824000666-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Software Impacts\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2665963824000666\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Impacts","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665963824000666","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
SSFCM-FWCW: Semi-Supervised Fuzzy C-Means method based on Feature-Weight and Cluster-Weight learning
SSFCM-FWCW (Feature-Weight and Cluster-Weight based Semi-Supervised Fuzzy C-Means) is a soft clustering method. It incorporates supplementary label information to enhance the clustering quality. An adaptive local feature weighting technique is utilized to weight features based on their significance within specific clusters. Additionally, an adaptive weighting technique is applied to diminish the sensitivity to the initial center selection, effectively distinguishing between the effects of various clusters. The conjunction of label information and adaptive weighting results in an optimal fuzzy c-means clustering with an insight into the importance of individual features and clusters. An open-source Matlab implementation of SSFCM-FWCW is available.
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