2019/12/05 by Hossein Kamalzadeh, Kamalzadeh, Hossein, Abbas Ahmadi +3
Computer Science · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.1912.02405
openalex publication_date 2019/12/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently there has been an increase in the studies on time-series data mining\nspecifically time-series clustering due to the vast existence of time-series in\nvarious domains. The large volume of data in the form of time-series makes it\nnecessary to employ various techniques such as clustering to understand the\ndata and to extract information and hidden patterns. In the field of clustering\nspecifically, time-series clustering, the most important aspects are the\nsimilarity measure used and the algorithm employed to conduct the clustering.\nIn this paper, a new similarity measure for time-series clustering is developed\nbased on a combination of a simple representation of time-series, slope of each\nsegment of time-series, Euclidean distance and the so-called dynamic time\nwarping. It is proved in this paper that the proposed distance measure is\nmetric and thus indexing can be applied. For the task of clustering, the\nParticle Swarm Optimization algorithm is employed. The proposed similarity\nmeasure is compared to three existing measures in terms of various criteria\nused for the evaluation of clustering algorithms. The results indicate that the\nproposed similarity measure outperforms the rest in almost every dataset used\nin this paper.\n