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Abstract: Time series is a very popular type of data which exists in many domains. Clustering time series data has a wide range of applications and has attracted researchers from a wide range of discipline. In this paper a novel algorithm for shape based time series clustering is proposed. It can reduce the size of data, improve the efficiency and not reduce the effects by using the principle of complex network. Firstly, one-nearest neighbor network is built based on the similarity of time series objects. In this step, triangle distance is used to measure the similarity. Of the neighbor network each node represents one time series object and each link denotes neighbor relationship between nodes. Secondly, the nodes with high degrees are chosen and used to cluster. In clustering process, dynamic time warping distance function and hierarchical clustering algorithm are applied. Thirdly, some experiments are executed on synthetic and real data. The results show that the proposed algorithm has good performance on efficiency and effectiveness. [Copyright &y& Elsevier]

• Propose a feature grid-based CNN model, FGCN, on traditional data. • Propose methods of converting instance with form of 1-d vector to feature grid. • The performance of FGCN model has reached the state-of-the-art technique XGBoost. • The positions of features in the grid have little influence on prediction accuracy. • Fully connected layers in CNN give little marginal classification performance. Convolutional neural networks (ConvNets) have been applied to various types of data, including image, text, and speech, but not to traditional data. In this study, traditional data are defined as data whose features have no spatial or temporal dependencies but might have statistical correlations. We construct a feature grid-based ConvNet (FGCN) model for classification tasks on traditional data. The FGCN model is composed of two functional parts: The first is used to convert traditional data in the form of a 1-D feature vector into a 1-D, 2-D, or higher-dimensional feature grid; and the second is a ConvNet classifier for the converted data. The experimental results show that the FGCN model performs well; therefore, it is worth considering this model for classification tasks on traditional data. [ABSTRACT FROM AUTHOR]