The increasing capability to collect data gives us the possibility to collect a massive amount of heterogeneous data. Among the heterogeneous data available, time-series represents a mother lode of information yet to be fully explored. Current data mining techniques have several shortcomings while analyzing time-series, especially when more than one time-series, i.e., multi-dimensional time-series, should be analyzed together to extract knowledge from the data. In this context, we present K-MDTSC (K-Multi-Dimensional Time-Series Clustering), a novel clustering algorithm specifically designed to deal with multi-dimensional time-series. Firstly, we demonstrate K-MDTSC capability to group multi-dimensional time-series using synthetic datasets. We compare K-MDTSC results with k-Shape, a state-of-art time-series clustering algorithm based on K-means. Our results show both K-MDTSC and k-Shape create good clustering results. However, K-MDTSC outperforms k-Shape when complicating the synthetic dataset. Secondly, we apply K-MDTSC in a real case scenario where we are asked to replace a scheduled maintenance with a predictive approach. To this end, we create a generalized pipeline to process data from a real industrial plant welding process. We apply K-MDTSC to create clusters of weldings based on their welding shape. Our results show that K-MDTSC identifies different welding profiles, but that the aging of the electrode does not negatively impact the welding process.

中文翻译：

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K-MDTSC：K多维时间序列聚类算法

收集数据的能力不断增强，使我们有可能收集大量的异构数据。在可用的异构数据中，时间序列代表着尚未被充分探索的信息母体。当前的数据挖掘技术在分析时间序列时存在多个缺点，尤其是在应同时分析多个时间序列（即多维时间序列）以从数据中提取知识时。在这种情况下，我们提出了K-MDTSC（K多维时间序列聚类），这是一种专门设计用于处理多维时间序列的新颖聚类算法。首先，我们展示了K-MDTSC使用合成数据集对多维时间序列进行分组的能力。我们比较K-MDTSCk-Shape的结果，这是一种基于K-means的最新时间序列聚类算法。我们的结果表明，K-MDTSC和k-Shape均可创建良好的聚类结果。但是，在使合成数据集复杂时，K-MDTSC的性能优于k-Shape。其次，我们在实际情况下应用K-MDTSC，要求我们用预测性方法替换计划的维护。为此，我们创建了一条通用管道来处理来自实际工厂焊接过程的数据。我们应用K- MDTSC根据焊接形状创建焊接簇。我们的结果表明，K-MDTSC 可以识别不同的焊接曲线，但是电极的老化不会对焊接过程产生负面影响。