论文标题
从时间序列到欧几里得空间：关于时间聚类的空间转换
From Time Series to Euclidean Spaces: On Spatial Transformations for Temporal Clustering
论文作者
论文摘要
时间数据的无监督聚类在机器学习中既具有挑战性又至关重要。在本文中，我们表明，当输入数据中存在变化的采样率和高维度时，传统的聚类方法，特定时间序列的特定时间序列甚至基于深度学习的替代方案都可以很好地推广。 We propose a novel approach to temporal clustering, in which we (1) transform the input time series into a distance-based projected representation by using similarity measures suitable for dealing with temporal data,(2) feed these projections into a multi-layer CNN-GRU autoencoder to generate meaningful domain-aware latent representations, which ultimately (3) allow for a natural separation of clusters beneficial for most important traditional clustering algorithms.我们从各个域中评估了时间序列数据集的方法，并表明它不仅在所有情况下都胜过现有的方法，最高可达32％，而且还具有强大的且构成可忽略的计算开销。
Unsupervised clustering of temporal data is both challenging and crucial in machine learning. In this paper, we show that neither traditional clustering methods, time series specific or even deep learning-based alternatives generalise well when both varying sampling rates and high dimensionality are present in the input data. We propose a novel approach to temporal clustering, in which we (1) transform the input time series into a distance-based projected representation by using similarity measures suitable for dealing with temporal data,(2) feed these projections into a multi-layer CNN-GRU autoencoder to generate meaningful domain-aware latent representations, which ultimately (3) allow for a natural separation of clusters beneficial for most important traditional clustering algorithms. We evaluate our approach on time series datasets from various domains and show that it not only outperforms existing methods in all cases, by up to 32%, but is also robust and incurs negligible computation overheads.
