基于随机特征的非线性数据属性加权核模糊聚类

Qiushi Tian, Jin Zhou, Shiyuan Han, Lin Wang, Yuehui Chen
{"title":"基于随机特征的非线性数据属性加权核模糊聚类","authors":"Qiushi Tian, Jin Zhou, Shiyuan Han, Lin Wang, Yuehui Chen","doi":"10.1109/SPAC49953.2019.237882","DOIUrl":null,"url":null,"abstract":"Traditional kernel clustering methods are useful in dealing with non-linear data, but the high-dimensional kernel space obtained by kernel mapping is an abstract concept, which is difficult to be determined. The kernel mapping between raw data space and kernel space needs high computational complexity which is burdensome for hardware. At the same time, due to the unknown nature of kernel space, traditional kernel clustering methods cannot process data with the consideration of different importance among dimensions, i.e., discover the hidden feature subset of high-dimensional sparse data. To overcome these limitations, we put forward a novel random Fourier feature based attribute-weighed kernel fuzzy c-means clustering algorithm (RFF-WKFCM). This method employs RFF map to generate low-rank random features, and performs fuzzy c-means clustering with attribute weight entropy regularization in this feature space, which greatly reduces the computational complexity. What is more, the adoption of the maximum entropy technique ensures the optimal distribution of attribute weights, which stimulate important dimensions play a greater role in the clustering process. The proposed method shows good performance on the experiments of ring data set compared with other fuzzy clutering methods.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Random Feature Based Attribute-weighed Kernel Fuzzy Clustering for Non-linear Data\",\"authors\":\"Qiushi Tian, Jin Zhou, Shiyuan Han, Lin Wang, Yuehui Chen\",\"doi\":\"10.1109/SPAC49953.2019.237882\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional kernel clustering methods are useful in dealing with non-linear data, but the high-dimensional kernel space obtained by kernel mapping is an abstract concept, which is difficult to be determined. The kernel mapping between raw data space and kernel space needs high computational complexity which is burdensome for hardware. At the same time, due to the unknown nature of kernel space, traditional kernel clustering methods cannot process data with the consideration of different importance among dimensions, i.e., discover the hidden feature subset of high-dimensional sparse data. To overcome these limitations, we put forward a novel random Fourier feature based attribute-weighed kernel fuzzy c-means clustering algorithm (RFF-WKFCM). This method employs RFF map to generate low-rank random features, and performs fuzzy c-means clustering with attribute weight entropy regularization in this feature space, which greatly reduces the computational complexity. What is more, the adoption of the maximum entropy technique ensures the optimal distribution of attribute weights, which stimulate important dimensions play a greater role in the clustering process. The proposed method shows good performance on the experiments of ring data set compared with other fuzzy clutering methods.\",\"PeriodicalId\":410003,\"journal\":{\"name\":\"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPAC49953.2019.237882\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPAC49953.2019.237882","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

传统的核聚类方法在处理非线性数据时很有用,但核映射得到的高维核空间是一个抽象的概念,难以确定。原始数据空间和内核空间之间的核映射需要很高的计算复杂度,这对硬件来说是一种负担。同时,由于核空间的未知性质,传统的核聚类方法无法在处理数据时考虑到不同维度的重要性,即无法发现高维稀疏数据的隐藏特征子集。为了克服这些局限性,我们提出了一种基于随机傅立叶特征的属性加权核模糊c均值聚类算法(RFF-WKFCM)。该方法利用RFF映射生成低秩随机特征,并在该特征空间中进行属性权熵正则化的模糊c均值聚类,大大降低了计算复杂度。此外,最大熵技术的采用保证了属性权值的最优分布,使重要维度在聚类过程中发挥更大的作用。与其他模糊聚类方法相比,该方法在环形数据集的实验中表现出良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Random Feature Based Attribute-weighed Kernel Fuzzy Clustering for Non-linear Data
Traditional kernel clustering methods are useful in dealing with non-linear data, but the high-dimensional kernel space obtained by kernel mapping is an abstract concept, which is difficult to be determined. The kernel mapping between raw data space and kernel space needs high computational complexity which is burdensome for hardware. At the same time, due to the unknown nature of kernel space, traditional kernel clustering methods cannot process data with the consideration of different importance among dimensions, i.e., discover the hidden feature subset of high-dimensional sparse data. To overcome these limitations, we put forward a novel random Fourier feature based attribute-weighed kernel fuzzy c-means clustering algorithm (RFF-WKFCM). This method employs RFF map to generate low-rank random features, and performs fuzzy c-means clustering with attribute weight entropy regularization in this feature space, which greatly reduces the computational complexity. What is more, the adoption of the maximum entropy technique ensures the optimal distribution of attribute weights, which stimulate important dimensions play a greater role in the clustering process. The proposed method shows good performance on the experiments of ring data set compared with other fuzzy clutering methods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信