发表机构
Univ Rennes; Ensai; CNRS; CREST（雷恩大学; 国立统计与信息分析学校; 法国国家科学研究中心; CREST研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出CLUES-WEASEL时间序列聚类算法，结合无监督特征提取、主成分分析和k-means，在性能和速度上均优于现有方法。
AI 中文摘要
时间序列数据在许多现实应用和众多领域中非常常见，人们对使用机器学习进行自动化信息提取的兴趣日益增长。其中一个子领域是时间序列聚类，即以无监督方式在一组时间序列中识别聚类。大多数时间序列聚类算法都面临同样的平衡难题：它们要么以聚类性能换取更快的运行时间，要么反之。我们提出了一种新颖的时间序列聚类算法，称为CLUES-WEASEL，其全称为CLustering with the UnsupervisEd Second version of Word ExtrAction for time SEries cLassification（使用无监督第二版时间序列分类词提取进行聚类）。CLUES-WEASEL使用WEASEL 2.0（一种时间序列分类算法）变换步骤的无监督版本提取特征，然后使用主成分分析对这些特征进行降维，最后使用$k$-means算法基于这些降维后的提取特征进行聚类。通过大量实验，我们证明CLUES-WEASEL显著优于任何其他现有时间序列聚类算法，同时比任何最先进的算法（快得多）更快。我们还表明，CLUES-WEASEL的架构可以很好地与其他时间序列特征提取算法配合使用。我们的发现凸显了CLUES-WEASEL在时间序列聚类中的相关性。
英文摘要
Time series data is very common in many real-world applications and in numerous domains, with increasing interest for automated information extraction using machine learning. One of these subfields is time series clustering, which consists in identifying clusters among a set of time series in an unsupervised fashion. Most time series clustering algorithms suffer from the same balancing act: they trade clustering performance for faster runtimes or vice versa. We present a novel time series clustering algorithm that we call CLUES-WEASEL, which stands for CLustering with the UnsupervisEd Second version of Word ExtrAction for time SEries cLassification. CLUES-WEASEL extracts features using the unsupervised version of the transformation step of WEASEL 2.0, which is a time series classification algorithm, then reduces these features using principal component analysis, and finally performs clustering with the $k$-means algorithm using these reduced extracted features. Through extensive experiments, we prove that CLUES-WEASEL is significantly better than any other existing time series clustering algorithm while being (much) faster than any state-of-the-art one. We also show that the architecture of CLUES-WEASEL can work well with other time series feature extraction algorithms. Our findings highlight the relevance of CLUES-WEASEL for time series clustering.
