This is the repository for our paper titled "Graph-based clustering for time series data". This paper has been accepted at 2021 IEEE International Conference on Big Data (Big Data)
Clustering is an important data mining task that consists of grouping data without prior knowledge of classes. In recent years, with the rise of big data and its various applications, unsupervised learning such as clustering algorithms has attracted massive interest in the data mining field. In the case of time series data, temporal changes are difficult to detect, which makes the clustering for temporal sequence data more complex than traditional vector data. In this paper, we propose a new model that generates graphs from time series data to preserve important relations between different data points. In particular, every time series data will be considered as a node, then edges will be added between nodes if the dynamic time warping distances of time series data achieve a specific threshold, then a spectral clustering algorithm is applied to the generated graph. Our results show that our new proposal time series graph representation outperforms state-of-the-art clustering algorithms.
