统一时间序列聚类的k-means框架，让不同方法可公平比较
On time series clustering with k-means
- 提出端到端的Lloyd算法框架，统一距离函数使用方式
- 在7种主流算法上验证，性能差异主要由距离函数决定
- 适合想对比聚类方法或重现实验的研究者
时间序列聚类长期依赖基于距离的划分聚类方法，其中最流行的是将k-means（又称Lloyd算法）改进以捕捉时间依赖性。然而，这些算法常采用不同的配置方式，如初始化策略等关键参数，导致难以公平比较。本文提出一种基于Lloyd算法的标准时间序列聚类（TSCL）模型，采用端到端设计，将专用距离函数同时应用于分配、初始化和停止条件。该统一框架使我们能够更准确地将聚类性能差异归因于距离函数本身，而非配置差异。通过该框架，可对七种主流的Lloyd-based TSCL算法进行公平比较。
原文摘要 · Abstract (English)
There is a long history of research into time series clustering using distance-based partitional clustering. Many of the most popular algorithms adapt k-means (also known as Lloyd's algorithm) to exploit time dependencies in the data by specifying a time series distance function. However, these algorithms are often presented with k-means configured in various ways, altering key parameters such as the initialisation strategy. This variability makes it difficult to compare studies because k-means is known to be highly sensitive to its configuration. To address this, we propose a standard Lloyd's-based model for TSCL that adopts an end-to-end approach, incorporating a specialised distance function not only in the assignment step but also in the initialisation and stopping criteria. By doing so, we create a unified structure for comparing seven popular Lloyd's-based TSCL algorithms. This common framework enables us to more easily attribute differences in clustering performance to the distance function itself, rather than variations in the k-means configuration.
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