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Hierarchical Clustering of Time Series Based on Linear Information Granules

机译：基于线性信息颗粒的时间序列的分层聚类

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摘要

Time series clustering is one of the main tasks in time series data mining. In this paper, a new time series clustering algorithm is proposed based on linear information granules. First, we improve the identification method of fluctuation points using threshold set, which represents the main trend information of the original time series. Then using fluctuation points as segmented nodes, we segment the original time series into several information granules, and linear function is used to represent the information granules. With information granulation, a granular time series consisting of several linear information granules replaces the original time series. In order to cluster time series, we then propose a linear information granules based segmented matching distance measurement (LIG_SMD) to calculate the distance between every two granular time series. In addition, hierarchical clustering method is applied based on the new distance (LIG_SMD_HC) to get clustering results. Finally, some public and real datasets about time series are experimented to examine the effectiveness of the proposed algorithm. Specifically, Euclidean distance based hierarchical clustering (ED_HC) and Dynamic Time Warping distance based hierarchical clustering (DTW_HC) are used as the compared algorithms. Our results show that LIG_SMD_HC is better than ED_HC and DTW_HC in terms of F-Measure and Accuracy.
机译：时间序列聚类是时间序列数据挖掘中的主要任务之一。本文基于线性信息颗粒提出了一种新的时序序列聚类算法。首先，我们使用阈值集提高波动点的识别方法，这代表了原始时间序列的主要趋势信息。然后使用波动点作为分段节点，我们将原始时间序列分段为几个信息颗粒，并且线性函数用于表示信息颗粒。利用信息造粒，由几个线性信息颗粒组成的粒状时间序列取代了原始的时间序列。为了培养时间序列，我们提出了一种基于线性信息颗粒的分段匹配距离测量（Lig_SMD），以计算每两个颗粒时间序列之间的距离。此外，基于新距离（LIG_SMD_HC）来应用分层群集方法以获取聚类结果。最后，一些关于时间序列的公共和实际数据集进行了实验，以检查所提出的算法的有效性。具体地，基于欧几里德距离的分层聚类（ED_HC）和动态时间基于距离基于距离的分层聚类（DTW_HC）作为比较算法。我们的结果表明，在F测量和准确性方面，Lig_SMD_HC比ED_HC和DTW_HC更好。

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