监测聚类解决方案的变化:模型和应用综述

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2023-11-03 DOI:10.1155/2023/7493623
Muhammad Atif, Muhammad Shafiq, Muhammad Farooq, Gohar Ayub, Friedrich Leisch, Muhammad Ilyas
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引用次数: 0

摘要

本文全面回顾了用于监控数据流中聚类解决方案演变的应用程序和算法。聚类技术是一种无监督学习问题,涉及在大型数据集中识别自然子组。与监督学习模型相比,聚类是一种数据挖掘技术,用于检索输入数据集中的隐藏模式。聚类解决方案反映了导致项目之间高度相似的机制。一些应用包括模式识别、知识发现和市场细分。然而,随着时间的推移,许多现代应用程序生成流或时态数据集,其中的模式不是固定的,可能会随着时间的推移而变化。在本文中,变化检测是在连续时间点从流数据集获得的集群解决方案中识别差异的过程。在本文中,我们简要回顾了文献中介绍的用于监测数据流中集群演化的模型/算法。监测流数据集中聚类解决方案的变化在决策和未来预测中起着至关重要的作用。当然,它的应用范围很广,无法在一项研究中涵盖,但本文将重点介绍一些最常见的应用。
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Monitoring Changes in Clustering Solutions: A Review of Models and Applications
This article comprehensively reviews the applications and algorithms used for monitoring the evolution of clustering solutions in data streams. The clustering technique is an unsupervised learning problem that involves the identification of natural subgroups in a large dataset. In contrast to supervised learning models, clustering is a data mining technique that retrieves the hidden pattern in the input dataset. The clustering solution reflects the mechanism that leads to a high level of similarity between the items. A few applications include pattern recognition, knowledge discovery, and market segmentation. However, many modern-day applications generate streaming or temporal datasets over time, where the pattern is not stationary and may change over time. In the context of this article, change detection is the process of identifying differences in the cluster solutions obtained from streaming datasets at consecutive time points. In this paper, we briefly review the models/algorithms introduced in the literature to monitor clusters’ evolution in data streams. Monitoring the changes in clustering solutions in streaming datasets plays a vital role in policy-making and future prediction. Of course, it has a wide range of applications that cannot be covered in a single study, but some of the most common are highlighted in this article.
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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