检索结果-南通市图书馆

subspace clustering in High-Dimensional Data Streams:A Systematic Literature Review

Computers, Materials & Continua 2023年第5期75卷 4649-4668页

作者： Nur Laila Ab Ghani Izzatdin Abdul Aziz Said Jadid AbdulKadir Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Centre for Research in Data Science(CeRDAS) Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia

clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space.subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams.Data streams are not only high-dimensional,but also unbounded and evolving.This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams.Although many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data streams.Therefore,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments.The review follows a systematic methodological approach and includes 18 articles for the final analysis.The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams.The main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation measures.Most algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering stage.The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters.Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers.Future work can focus on the clustering framework,parameter optimization,subspace search techniques,memory-efficient

关键词： clustering subspace clustering projected clustering data stream stream clustering high dimensionality evolving data stream concept drift

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

PMSSC:Parallelizable multi-subset based self-expressive model for subspace clustering

引用

Computational Visual Media 2023年第3期9卷 479-494页

作者： Katsuya Hotta Takuya Akashi Shogo Tokai Chao Zhang Department of Engineering University of FukuiFukui-shi 910-8507Japan Faculty of Science and Engineering Iwate UniversityMorioka-shi 020-8551Japan

subspace clustering methods which embrace a self-expressive model that represents each data point as a linear combination of other data points in the dataset provide powerful unsupervised learning techniques.However,when dealing with large datasets,representation of each data point by referring to all data points via a dictionary suffers from high computational complexity.To alleviate this issue,we introduce a parallelizable multi-subset based self-expressive model(PMS)which represents each data point by combining multiple subsets,with each consisting of only a small proportion of the samples.The adoption of PMS in subspace clustering(PMSSC)leads to computational advantages because the optimization problems decomposed over each subset are small,and can be solved efficiently in parallel.Furthermore,PMSSC is able to combine multiple self-expressive coefficient vectors obtained from subsets,which contributes to an improvement in self-expressiveness.Extensive experiments on synthetic and real-world datasets show the efficiency and effectiveness of our approach in comparison to other methods.

关键词： subspace clustering self-expressive model big data subsetting

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

subspace clustering through attribute clustering

引用

Frontiers of Electrical and Electronic Engineering in China 2008年第1期3卷 44-48页

作者： Kun NIU Shubo ZHANG Junliang CHEN State Key Laboratory of Networking and Switching Technology Beijing 100876China Department of Strategy Research China Telecom Beijing Research InstituteBeijing 100035China

Many recently proposed subspace clustering methods suffer from two severe problems.First,the algorithms typically scale exponentially with the data dimensionality or the subspace dimensionality of clusters.Second,the clustering results are often sensitive to input parameters.In this paper,a fast algorithm of subspace clustering using attribute clustering is proposed to overcome these limitations.This algorithm first filters out redundant attributes by computing the Gini coef-ficient.To evaluate the correlation of every two non-redundant attributes,the relation matrix of non-redund-ant attributes is constructed based on the relation function of two dimensional united Gini coefficients.After applying an overlapping clustering algorithm on the relation matrix,the candidate of all interesting subspaces is achieved.Finally,all subspace clusters can be derived by clustering on interesting subspaces.Experiments on both synthesis and real datasets show that the new algorithm not only achieves a significant gain of runtime and quality to find subspace clusters,but also is insensitive to input parameters.

关键词： subspace clustering high dimensional data attribute clustering

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

NONNEGATIVE MATRIX FACTORIZATION WITH BAND CONSTRAINT

引用

Journal of Computational Mathematics 2018年第6期36卷 761-775页

作者： Xiangxiang Zhu Jicheng Li Zhuosheng Zhang School of Mathematics and Statistics Xi'an Jiaotong University Xi'an 710049 China

In this paper, we study a band constrained nonnegative matrix factorization （band NMF） problem： for a given nonnegative matrix Y, decompose it as Y ≈ AX with A a nonnegative matrix and X a nonnegative block band matrix. This factorization model extends a single low rank subspace model to a mixture of several overlapping low rank subspaces, which not only can provide sparse representation, but also can capture signifi- cant grouping structure from a dataset. Based on overlapping subspace clustering and the capture of the level of overlap between neighbouring subspaces, two simple and practical algorithms are presented to solve the band NMF problem. Numerical experiments on both synthetic data and real images data show that band NMF enhances the performance of NMF in data representation and processing.

关键词： Nonnegative matrix factorization Band structure subspace clustering Sparserepresentation Image compression

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Shape matching and retrieval based on multiple feature descriptors

引用

Computer Aided Drafting,Design and Manufacturing 2013年第1期23卷 71-78页

作者： WANG Weiming LIU Xiuping LIU Ligang School of Mathematical Sciences Dalian University of Technology School of Mathematical Sciences University of Science and Technology of China

A lot of 3D shape descriptors for 3D shape retrieval have been presented so far. This paper proposes a new mechanism, which employs several existing global and local 3D shape descriptors as input. With the sparse theory, some descriptors which play the most important role in measuring similarity between query model and the model in the dataset are selected automatically and an affinity matrix is constructed. Spectral clustering method can be implemented to this affinity matrix. Spectral embedding of this affinity matrix can be applied to retrieval, which integrating almost all the advantages of selected descriptors. In order to verify the performance of our approach, we perform experimental comparisons on Princeton Shape Benchmark database. Test results show that our method is a pose-oblivious, efficient and robustness method for either complete or incomplete models.

关键词： similarity invariant shape descriptor subspace clustering

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Differencial-clustering:Mining Bicluster Based on Weighted Graph in Microarray Dataset

Differencial-Clustering:Mining Bicluster Based on Weighted G...

引用

2013年中国智能自动化学术会议

作者： Jingni Diao Cuifang Zheng Jilan Zhang Jiaju Wu Institution of Computer Application China Academy of Engineering Physics

In this study,we discuss methods of mining differential biclusters on gene expression data.A differential bicluster is a set of genes corresponding to samples which have substantially different expression values in two sample classes.These differential clusters may have meaningful biology significance that some specified experimental conditions are key factor to gene expression values and certain genes are sensitive to these conditions.In general,most of bicluster algorithms follow a respective clustering framework that they mine clusters in two classes.However,this strategy leads to low efficiency in term of time.We proposed a novel algorithm:Differencial-clustering with the strategy which mine differential biclusters directly on weighted graph corresponding to two sample classes.The main contribution of our algorithm is that we avoid the respective cluster generation in two classes and comparison strategy.We compare our algorithm with standard subspace clustering method,the experimental result analysis demonstrate our main contribution.

关键词： Differential bicluster subspace clustering Differential graphs

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

A Novel subspace Outlier Detection Approach in High Dimensional Data Sets

A Novel Subspace Outlier Detection Approach in High Dimensio...

引用

2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Jinsong Leng School of Computer and Security Edith Cowan University

Many real applications are required to detect outliers in high dimensional data sets. The major difficulty of mining outliers lies on the fact that outliers are often embedded in subspaces. No efficient methods are available in general for subspace-based outlier detection. Most existing subspace-based outlier detection methods identify outliers by searching for abnormal sparse density units in subspaces. In this paper, we present a novel approach for finding outliers in the ’interesting’ subspaces. The interesting subspaces are strongly correlated with `good’ clusters. This approach aims to group the meaningful subspaces and then identify outliers in the projected subspaces. In doing so, an extension to the subspace-based clustering algorithm is proposed so as to find the ’good’ subspaces, and then outliers are identified in the projected subspaces using some classical outlier detection techniques such as distance-based and density-based algorithms. Comprehensive case studies are conducted using various types of subspace clustering and outlier detection algorithms. The experimental results demonstrate that the proposed method can detect outliers effectively and efficiently in high dimensional data sets.

关键词： Data Mining subspace clustering Outlier Detection Dimensional Reduction

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

一种基于单调递减阈值函数的高维数据集聚类算法

一种基于单调递减阈值函数的高维数据集聚类算法

引用

第二十二届中国数据库学术会议

作者：周晓云孙志挥张柏礼胡文瑜东南大学计算机科学与工程系福建工程学院计算机与信息科学系

1引言聚类分析是数据挖掘领域中的一项重要的研究课题,它既可以作为一个单独的工具以发现数据库中数据分布的一些深入的信息,也可以作为其他数据挖掘分析算法的一个预处理步骤。目前,高维数据集的高效聚类已成为聚类分析算法的巨大挑战。

关键词： clustering algorithms subspace clustering Optimal partition Threshold function

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：