找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Clustering High--Dimensional Data; First International Francesco Masulli,Alfredo Petrosino,Stefano Rovett Conference proceedings 2015 Spri

[复制链接]
查看: 36789|回复: 43
发表于 2025-3-21 18:59:27 | 显示全部楼层 |阅读模式
书目名称Clustering High--Dimensional Data
副标题First International
编辑Francesco Masulli,Alfredo Petrosino,Stefano Rovett
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Clustering High--Dimensional Data; First International  Francesco Masulli,Alfredo Petrosino,Stefano Rovett Conference proceedings 2015 Spri
描述.This book constitutes the proceedings of the International Workshop on Clustering High-Dimensional Data, CHDD 2012, held in Naples, Italy, in May 2012. ..The 9 papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the general subject and issues of high-dimensional data clustering; present examples of techniques used to find and investigate clusters in high dimensionality; and the most common approach to tackle dimensionality problems, namely, dimensionality reduction and its application in clustering. .
出版日期Conference proceedings 2015
关键词big data clustering; dimensionality reduction; high dimensional data analysis; machine learning; time se
版次1
doihttps://doi.org/10.1007/978-3-662-48577-4
isbn_softcover978-3-662-48576-7
isbn_ebook978-3-662-48577-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2015
The information of publication is updating

书目名称Clustering High--Dimensional Data影响因子(影响力)




书目名称Clustering High--Dimensional Data影响因子(影响力)学科排名




书目名称Clustering High--Dimensional Data网络公开度




书目名称Clustering High--Dimensional Data网络公开度学科排名




书目名称Clustering High--Dimensional Data被引频次




书目名称Clustering High--Dimensional Data被引频次学科排名




书目名称Clustering High--Dimensional Data年度引用




书目名称Clustering High--Dimensional Data年度引用学科排名




书目名称Clustering High--Dimensional Data读者反馈




书目名称Clustering High--Dimensional Data读者反馈学科排名




单选投票, 共有 0 人参与投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 20:22:38 | 显示全部楼层
What are Clusters in High Dimensions and are they Difficult to Find?,low-dimensional data set. Concentration of norm is one of the phenomena from which high-dimensional data sets can suffer. It means that in high dimensions – under certain general assumptions – the relative distances from any point to its closest and farthest neighbour tend to be almost identical. Si
发表于 2025-3-22 03:54:37 | 显示全部楼层
发表于 2025-3-22 05:50:11 | 显示全部楼层
发表于 2025-3-22 10:05:23 | 显示全部楼层
发表于 2025-3-22 13:42:54 | 显示全部楼层
发表于 2025-3-22 20:15:38 | 显示全部楼层
发表于 2025-3-22 21:50:34 | 显示全部楼层
发表于 2025-3-23 03:34:40 | 显示全部楼层
A Rough Fuzzy Perspective to Dimensionality Reduction,ny real–world problems. The focus of rough set theory is on the ambiguity caused by limited discernibility of objects in the domain of discourse; granules are formed as objects and are drawn together by the limited discernibility among them. On the other hand, membership functions of fuzzy sets enab
发表于 2025-3-23 09:34:07 | 显示全部楼层
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-7-4 10:30
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表