FastMap Projection for High-Dimensional Data: A Cluster Ensemble Approach

Imran Khan, Kamen Ivanov, Qingshan Jiang
{"title":"FastMap Projection for High-Dimensional Data: A Cluster Ensemble Approach","authors":"Imran Khan, Kamen Ivanov, Qingshan Jiang","doi":"10.14257/IJDTA.2016.9.12.28","DOIUrl":null,"url":null,"abstract":"High-dimensional data with many features present a significant challenge to current clustering algorithms.Sparsity, noise, and correlation of features are common properties of high-dimensional data.Another essential aspect is that clusters in such data often exist in various subspaces. Ensemble clusteringis emerging as a leading technique for improving robustness, stability, and accuracy of high-dimensional data clusterings. In this paper, we propose FastMap projection for generating subspace component data sets from high-dimensional data. By using component data sets, we create component clusterings and provides a new objective function that ensembles them by maximizing the average similarity between component clusterings and final clustering. Compared with the random sampling and random projection methods, the component clusterings by FastMap projection showed high average clustering accuracy without sacrificing clustering diversity in synthetic data analysis. We conducted a series of experiments\non real-world data sets from microarray, text, and image domains employing three subspace component data generation methods, three consensus functions, and a proposed objective function for ensemble clustering. The experiment results consistently demonstrated that the FastMap projection method with the proposed objection function provided the best ensemble clustering results for all data sets.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":"18 1","pages":"311-330"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of database theory and application","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14257/IJDTA.2016.9.12.28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

High-dimensional data with many features present a significant challenge to current clustering algorithms.Sparsity, noise, and correlation of features are common properties of high-dimensional data.Another essential aspect is that clusters in such data often exist in various subspaces. Ensemble clusteringis emerging as a leading technique for improving robustness, stability, and accuracy of high-dimensional data clusterings. In this paper, we propose FastMap projection for generating subspace component data sets from high-dimensional data. By using component data sets, we create component clusterings and provides a new objective function that ensembles them by maximizing the average similarity between component clusterings and final clustering. Compared with the random sampling and random projection methods, the component clusterings by FastMap projection showed high average clustering accuracy without sacrificing clustering diversity in synthetic data analysis. We conducted a series of experiments on real-world data sets from microarray, text, and image domains employing three subspace component data generation methods, three consensus functions, and a proposed objective function for ensemble clustering. The experiment results consistently demonstrated that the FastMap projection method with the proposed objection function provided the best ensemble clustering results for all data sets.
高维数据的快速映射投影:一种聚类集成方法
具有多种特征的高维数据对当前的聚类算法提出了重大挑战。特征的稀疏性、噪声和相关性是高维数据的共同特性。另一个重要方面是,此类数据中的集群通常存在于不同的子空间中。集成聚类正在成为提高高维数据聚类的鲁棒性、稳定性和准确性的主要技术。在本文中,我们提出了FastMap投影,用于从高维数据生成子空间组件数据集。通过使用组件数据集,我们创建组件聚类,并提供一个新的目标函数,通过最大化组件聚类和最终聚类之间的平均相似度来集成它们。与随机抽样和随机投影方法相比,FastMap投影方法在不牺牲聚类多样性的情况下,具有较高的平均聚类精度。我们对来自微阵列、文本和图像领域的真实数据集进行了一系列实验,采用了三种子空间分量数据生成方法、三种共识函数和一个集成聚类的目标函数。实验结果一致表明,基于目标函数的FastMap投影方法对所有数据集的集成聚类效果最好。
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 求助全文
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