PPGMMGA投影子空间上的模态聚类

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY
Luca Scrucca
{"title":"PPGMMGA投影子空间上的模态聚类","authors":"Luca Scrucca","doi":"10.1111/anzs.12360","DOIUrl":null,"url":null,"abstract":"<p>PPGMMGA is a projection pursuit (PP) algorithm aimed at detecting and visualising clustering structures in multivariate data. The algorithm uses the negentropy as PP index obtained by fitting Gaussian mixture models (GMMs) for density estimation and, then, exploits genetic algorithms (GAs) for its optimisation. Since the PPGMMGA algorithm is a dimension reduction technique specifically introduced for visualisation purposes, cluster memberships are not explicitly provided. In this paper a modal clustering approach is proposed for estimating clusters of projected data points. In particular, a modal EM algorithm is employed to estimate the modes corresponding to the local maxima in the projection subspace of the underlying density estimated using parsimonious GMMs. Data points are then clustered according to the domain of attraction of the identified modes. Simulated and real data are discussed to illustrate the proposed method and evaluate the clustering performance.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":"64 2","pages":"158-170"},"PeriodicalIF":0.8000,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.12360","citationCount":"1","resultStr":"{\"title\":\"Modal clustering on PPGMMGA projection subspace\",\"authors\":\"Luca Scrucca\",\"doi\":\"10.1111/anzs.12360\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>PPGMMGA is a projection pursuit (PP) algorithm aimed at detecting and visualising clustering structures in multivariate data. The algorithm uses the negentropy as PP index obtained by fitting Gaussian mixture models (GMMs) for density estimation and, then, exploits genetic algorithms (GAs) for its optimisation. Since the PPGMMGA algorithm is a dimension reduction technique specifically introduced for visualisation purposes, cluster memberships are not explicitly provided. In this paper a modal clustering approach is proposed for estimating clusters of projected data points. In particular, a modal EM algorithm is employed to estimate the modes corresponding to the local maxima in the projection subspace of the underlying density estimated using parsimonious GMMs. Data points are then clustered according to the domain of attraction of the identified modes. Simulated and real data are discussed to illustrate the proposed method and evaluate the clustering performance.</p>\",\"PeriodicalId\":55428,\"journal\":{\"name\":\"Australian & New Zealand Journal of Statistics\",\"volume\":\"64 2\",\"pages\":\"158-170\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2022-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.12360\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Australian & New Zealand Journal of Statistics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/anzs.12360\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Australian & New Zealand Journal of Statistics","FirstCategoryId":"100","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/anzs.12360","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 1

摘要

PPGMMGA是一种投影寻踪算法,旨在检测和可视化多变量数据中的聚类结构。该算法使用通过拟合高斯混合模型(GMMs)获得的负熵作为PP指数进行密度估计,然后利用遗传算法(GAs)进行优化。由于PPGMMGA算法是一种专门为可视化目的引入的降维技术,因此没有明确提供集群成员关系。本文提出了一种估计投影数据点聚类的模态聚类方法。特别地,使用模态EM算法来估计使用简约GMMs估计的底层密度的投影子空间中的局部最大值对应的模态。然后根据识别模式的吸引域对数据点进行聚类。通过仿真数据和真实数据对该方法进行了验证,并对聚类性能进行了评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Modal clustering on PPGMMGA projection subspace

Modal clustering on PPGMMGA projection subspace

PPGMMGA is a projection pursuit (PP) algorithm aimed at detecting and visualising clustering structures in multivariate data. The algorithm uses the negentropy as PP index obtained by fitting Gaussian mixture models (GMMs) for density estimation and, then, exploits genetic algorithms (GAs) for its optimisation. Since the PPGMMGA algorithm is a dimension reduction technique specifically introduced for visualisation purposes, cluster memberships are not explicitly provided. In this paper a modal clustering approach is proposed for estimating clusters of projected data points. In particular, a modal EM algorithm is employed to estimate the modes corresponding to the local maxima in the projection subspace of the underlying density estimated using parsimonious GMMs. Data points are then clustered according to the domain of attraction of the identified modes. Simulated and real data are discussed to illustrate the proposed method and evaluate the clustering performance.

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
Australian & New Zealand Journal of Statistics
Australian & New Zealand Journal of Statistics 数学-统计学与概率论
CiteScore
1.30
自引率
9.10%
发文量
31
审稿时长
>12 weeks
期刊介绍: The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association. The main body of the journal is divided into three sections. The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data. The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context. The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems.
×
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