聚类分析
计算机科学
数据挖掘
共识聚类
高维数据聚类
功能数据分析
数据科学
双聚类
模糊聚类
机器学习
情报检索
人工智能
CURE数据聚类算法
作者
Mimi Zhang,Andrew Parnell
出处
期刊:ACM Transactions on Knowledge Discovery From Data
[Association for Computing Machinery]
日期:2023-04-06
卷期号:17 (7): 1-34
摘要
Functional data clustering is to identify heterogeneous morphological patterns in the continuous functions underlying the discrete measurements/observations. Application of functional data clustering has appeared in many publications across various fields of sciences, including but not limited to biology, (bio)chemistry, engineering, environmental science, medical science, psychology, social science, and so on. The phenomenal growth of the application of functional data clustering indicates the urgent need for a systematic approach to develop efficient clustering methods and scalable algorithmic implementations. On the other hand, there is abundant literature on the cluster analysis of time series, trajectory data, spatio-temporal data, and so on, which are all related to functional data. Therefore, an overarching structure of existing functional data clustering methods will enable the cross-pollination of ideas across various research fields. We here conduct a comprehensive review of original clustering methods for functional data. We propose a systematic taxonomy that explores the connections and differences among the existing functional data clustering methods and relates them to the conventional multivariate clustering methods. The structure of the taxonomy is built on three main attributes of a functional data clustering method and therefore is more reliable than existing categorizations. The review aims to bridge the gap between the functional data analysis community and the clustering community and to generate new principles for functional data clustering.
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