Soft multi-modal data fusion

S. Coppock, L. Mazlack
{"title":"Soft multi-modal data fusion","authors":"S. Coppock, L. Mazlack","doi":"10.1109/FUZZ.2003.1209438","DOIUrl":null,"url":null,"abstract":"Clustering groups items together that are most similar to each other and sets those that are least similar into different clusters. Methods have been developed to cluster records in a data set that are of only qualitative or quantitative data. Data sets exist that contain a mix of qualitative (nominal and ordinal) and quantitative (discrete and continuous) data. Clustering records of mixed kinds of data is a difficult problem. A metric to measure the similarity between records of mixed data types is needed. Once a clustering is found, we do not know how to best evaluate the quality of the clustering when there is a mixture of data varieties.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZ.2003.1209438","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Clustering groups items together that are most similar to each other and sets those that are least similar into different clusters. Methods have been developed to cluster records in a data set that are of only qualitative or quantitative data. Data sets exist that contain a mix of qualitative (nominal and ordinal) and quantitative (discrete and continuous) data. Clustering records of mixed kinds of data is a difficult problem. A metric to measure the similarity between records of mixed data types is needed. Once a clustering is found, we do not know how to best evaluate the quality of the clustering when there is a mixture of data varieties.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
软多模态数据融合
聚类将彼此最相似的项组合在一起,并将最不相似的项设置到不同的聚类中。已经开发出方法,在只有定性或定量数据的数据集中对记录进行聚类。存在的数据集包含定性(名义和有序)和定量(离散和连续)数据的混合。混合类型数据的记录聚类是一个难题。需要一个度量来度量混合数据类型记录之间的相似性。一旦发现了聚类,当存在混合数据品种时,我们不知道如何最好地评估聚类的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Fuzzy flow-shop scheduling models based on credibility measure Morphological perceptrons with dendritic structure A validation procedure for fuzzy multiattribute decision making Context dependent information aggregation Traffic engineering with MPLS using fuzzy logic for application in IP networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1