Shahrood University of Technology Journal of AI and Data Mining 2322-5211 12 2 2024 04 01 A Novel Combination of Segmentation, Ensemble Clustering and Genetic Algorithm for Clustering Time Series 273 286 3249 10.22044/jadm.2024.14170.2526 EN Zahra Ghorbani Edinburgh Business School, Heriot-Watt University, Edinburgh, Scotland (UK). Ali Ghorbanian Department of Industrial Engineering, Esfarayen University of Technology, Esfarayen, Iran. Journal Article 2024 02 12 Increasing the accuracy of time-series clustering while reducing execution time is a primary challenge in the field of time-series clustering. Researchers have recently applied approaches, such as the development of distance metrics and dimensionality reduction, to address this challenge. However, using segmentation and ensemble clustering to solve this issue is a key aspect that has received less attention in previous research. In this study, an algorithm based on the selection and combination of the best segments created from a time-series dataset was developed. In the first step, the dataset was divided into segments of equal lengths. In the second step, each segment is clustered using a hierarchical clustering algorithm. In the third step, a genetic algorithm selects different segments and combines them using combinatorial clustering. The resulting clustering of the selected segments was selected as the final dataset clustering. At this stage, an internal clustering criterion evaluates and sorts the produced solutions. The proposed algorithm was executed on 82 different datasets in 10 repetitions. The results of the algorithm indicated an increase in the clustering efficiency of 3.07%, reaching a value of 67.40. The obtained results were evaluated based on the length of the time series and the type of dataset. In addition, the results were assessed using statistical tests with the six algorithms existing in the literature. https://jad.shahroodut.ac.ir/article_3249_870a5aeb351e8ea8a303f29fb657bf45.pdf