{"title":"不规则数据集的在线多尺度聚类算法","authors":"T. Guan, Yongling Yu, Tao Xue","doi":"10.1109/ICFCSA.2011.54","DOIUrl":null,"url":null,"abstract":"Clustering analysis has been widely applied to image segmentation, however, many of them cluster convex data sets with an offline way. This paper proposes a new online multiscale competitive learning approach for irregular data sets. This approach defines the multiscale learning rule for data sets using local scattering information of data and starts to cluster from only one prototype. When new vectors in another cluster are input, the initial prototype self-splits and produces new prototype to represent them. The process continues until there is no more vector. The clustering scale is defined by the local variances of clusters. We carried out experiments on irregular data sets and obtained better clustering results compared to K-means algorithm.","PeriodicalId":141108,"journal":{"name":"2011 International Conference on Future Computer Sciences and Application","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An Online Multiscale Clustering Algorithm for Irregular Data Sets\",\"authors\":\"T. Guan, Yongling Yu, Tao Xue\",\"doi\":\"10.1109/ICFCSA.2011.54\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clustering analysis has been widely applied to image segmentation, however, many of them cluster convex data sets with an offline way. This paper proposes a new online multiscale competitive learning approach for irregular data sets. This approach defines the multiscale learning rule for data sets using local scattering information of data and starts to cluster from only one prototype. When new vectors in another cluster are input, the initial prototype self-splits and produces new prototype to represent them. The process continues until there is no more vector. The clustering scale is defined by the local variances of clusters. We carried out experiments on irregular data sets and obtained better clustering results compared to K-means algorithm.\",\"PeriodicalId\":141108,\"journal\":{\"name\":\"2011 International Conference on Future Computer Sciences and Application\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Future Computer Sciences and Application\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICFCSA.2011.54\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Future Computer Sciences and Application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICFCSA.2011.54","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Online Multiscale Clustering Algorithm for Irregular Data Sets
Clustering analysis has been widely applied to image segmentation, however, many of them cluster convex data sets with an offline way. This paper proposes a new online multiscale competitive learning approach for irregular data sets. This approach defines the multiscale learning rule for data sets using local scattering information of data and starts to cluster from only one prototype. When new vectors in another cluster are input, the initial prototype self-splits and produces new prototype to represent them. The process continues until there is no more vector. The clustering scale is defined by the local variances of clusters. We carried out experiments on irregular data sets and obtained better clustering results compared to K-means algorithm.