Clustering methods for Categorical Time Series and Sequences : A scoping review
分类时间序列与序列聚类方法的范围综述AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接综述分类时间序列的聚类方法，支持时间序列分类；其跨学科应用不意味着研究对象均为宏观经济数据。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
分类时间序列与序列聚类方法的范围综述
- 研究问题
- 分类时间序列有哪些聚类方法，不同方法能否处理变长序列、多变量、连续时间、缺失和协变量等数据特征？
- 方法与思路
- 作者检索截至二〇二四年十一月的文献，将方法分为距离、特征和模型三类，并比较数据处理能力、灵活性及规模适应性。
- 材料陈述的贡献
- 摘要称，综述纳入一百二十四篇文章和一百二十九种方法，提出方法分类体系，并开发按数据特征筛选方法的网络应用。
- 阅读关注点
- 应核对检索与纳入标准、方法计数是否存在重叠、灵活性和可扩展性的评价依据，以及分类体系对自身数据是否适用。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；综述比较不等于统一实验验证，摘要未说明评价细则。
原文摘要
Objective: To provide an overview of clustering methods for categorical time series (CTS), a data structure commonly found in epidemiology, sociology, biology, and marketing, and to support method selection in regards to data characteristics. Methods: We searched PubMed, Web of Science, and Google Scholar, from inception up to November 2024 to identify articles that propose and evaluate clustering techniques for CTS. Methods were classified according to three major families -- distance-based, feature-based, and model-based -- and assessed on their ability to handle data challenges such as variable sequence length, multivariate data, continuous time, missing data, time-invariant covariates, and large data volumes. Results: Out of 14607 studies, we included 124 articles describing 129 methods, spanning domains such as artificial intelligence, social sciences, and epidemiology. Distance-based methods, particularly those using Optimal Matching, were most prevalent, with 56 methods. We identified 28 model-based methods, which demonstrated superior flexibility for handling complex data structures such as multivariate data, continuous time and time-invariant covariates. We also recorded 45 feature-based approaches, which were on average more scalable but less flexible. A searchable Web application was developed to facilitate method selection based on dataset characteristics ( https://cts-clustering-scoping-review-7sxqj3sameqvmwkvnzfynz.streamlit.app/ ) Discussion: While distance-based methods dominate, model-based approaches offer the richest modeling potential but are less scalable. Feature-based methods favor performance over flexibility, with limited support for complex data structures. Conclusion: This review highlights methodological diversity and gaps in CTS clustering. The proposed typology aims to guide researchers in selecting methods for their specific use cases.