时间序列数据的混合聚类方法

R. K. Prathipati, V. Shastri, Madhavi Kolukuluri, Radha Dharavathu, Donthireddy Sudheer Reddy, B. R. Krishna
{"title":"时间序列数据的混合聚类方法","authors":"R. K. Prathipati, V. Shastri, Madhavi Kolukuluri, Radha Dharavathu, Donthireddy Sudheer Reddy, B. R. Krishna","doi":"10.48112/bcs.v1i4.84","DOIUrl":null,"url":null,"abstract":"The clustering of data series was already demonstrated to provide helpful information in several fields. Initial data for the period is divided into sub-clusters Recorded in the data resemblance. The grouping of data series takes 3 categories, based on which users operate in frequencies or programming interfaces on original data explicitly or implicitly with the characteristics derived from physical information or through a framework based on raw material. The bases of series data grouping are provided. The conditions for the evaluation of the outcomes of grouping are multi-purpose time constant frequently employed in dataset grouping research. A clustering method splits data into different groups so that the resemblance between organisations is better. K-means++ offers an excellent convergence rate compared to other methods. To distinguish the correlation between items the maximum distance is employed. Distance measure metrics are frequently utilized with most methods by many academics. Genetic algorithm for the resolution of cluster issues is worldwide optimization technologies in recent times. The much more prevalent partitioning strategies of large volumes of data are K-Median & K-Median methods. This analysis is focusing on the multiple distance measures, such as Euclidean, Public Square and Shebyshev, hybrid K-means++ and PSO clubs techniques. Comparison to orgorganization-basedthods reveals an excellent classification result compared to the other methods with the K++ PSO method utilizing the Chebyshev distance measure.","PeriodicalId":176903,"journal":{"name":"Biomedicine and Chemical Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid Clustering Approach for Time Series Data\",\"authors\":\"R. K. Prathipati, V. Shastri, Madhavi Kolukuluri, Radha Dharavathu, Donthireddy Sudheer Reddy, B. R. Krishna\",\"doi\":\"10.48112/bcs.v1i4.84\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The clustering of data series was already demonstrated to provide helpful information in several fields. Initial data for the period is divided into sub-clusters Recorded in the data resemblance. The grouping of data series takes 3 categories, based on which users operate in frequencies or programming interfaces on original data explicitly or implicitly with the characteristics derived from physical information or through a framework based on raw material. The bases of series data grouping are provided. The conditions for the evaluation of the outcomes of grouping are multi-purpose time constant frequently employed in dataset grouping research. A clustering method splits data into different groups so that the resemblance between organisations is better. K-means++ offers an excellent convergence rate compared to other methods. To distinguish the correlation between items the maximum distance is employed. Distance measure metrics are frequently utilized with most methods by many academics. Genetic algorithm for the resolution of cluster issues is worldwide optimization technologies in recent times. The much more prevalent partitioning strategies of large volumes of data are K-Median & K-Median methods. This analysis is focusing on the multiple distance measures, such as Euclidean, Public Square and Shebyshev, hybrid K-means++ and PSO clubs techniques. Comparison to orgorganization-basedthods reveals an excellent classification result compared to the other methods with the K++ PSO method utilizing the Chebyshev distance measure.\",\"PeriodicalId\":176903,\"journal\":{\"name\":\"Biomedicine and Chemical Sciences\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedicine and Chemical Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.48112/bcs.v1i4.84\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedicine and Chemical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48112/bcs.v1i4.84","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

数据序列的聚类已经被证明可以在几个领域提供有用的信息。初始阶段的数据被分成子簇记录在数据相似度中。数据序列的分组分为三类,用户在频率或编程接口上对原始数据进行显式或隐式操作,这些操作具有从物理信息中获得的特征,或通过基于原材料的框架。为系列数据分组提供了依据。分组结果评价的条件是在数据集分组研究中经常使用的多用途时间常数。聚类方法将数据分成不同的组,以便组织之间的相似性更好。与其他方法相比,k -means++提供了出色的收敛速度。为了区分项目之间的相关性,使用了最大距离。距离度量是许多学者常用的度量方法之一。遗传算法是近年来解决聚类问题的一种世界性的优化技术。更流行的大容量数据分区策略是K-Median和K-Median方法。本文主要分析了欧几里得、Public Square和Shebyshev等多种距离度量方法,以及混合k -means++和PSO俱乐部技术。与基于组织的方法进行比较,发现利用Chebyshev距离度量的k++ PSO方法与其他方法相比具有良好的分类效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Clustering Approach for Time Series Data
The clustering of data series was already demonstrated to provide helpful information in several fields. Initial data for the period is divided into sub-clusters Recorded in the data resemblance. The grouping of data series takes 3 categories, based on which users operate in frequencies or programming interfaces on original data explicitly or implicitly with the characteristics derived from physical information or through a framework based on raw material. The bases of series data grouping are provided. The conditions for the evaluation of the outcomes of grouping are multi-purpose time constant frequently employed in dataset grouping research. A clustering method splits data into different groups so that the resemblance between organisations is better. K-means++ offers an excellent convergence rate compared to other methods. To distinguish the correlation between items the maximum distance is employed. Distance measure metrics are frequently utilized with most methods by many academics. Genetic algorithm for the resolution of cluster issues is worldwide optimization technologies in recent times. The much more prevalent partitioning strategies of large volumes of data are K-Median & K-Median methods. This analysis is focusing on the multiple distance measures, such as Euclidean, Public Square and Shebyshev, hybrid K-means++ and PSO clubs techniques. Comparison to orgorganization-basedthods reveals an excellent classification result compared to the other methods with the K++ PSO method utilizing the Chebyshev distance measure.
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