Abstract
In view of the low clustering efficiency and poor clustering effect of traditional hierarchical clustering algorithms, this paper measures distance based on dynamic time war** (DTW), and proposes an adaptive divisive analysis (DIANA) based on minimum spanning tree, which uses the solution of minimum spanning tree to replace the traditional clustering process so as to improve the algorithm performance and clustering quality. In the aspect of solving the minimum spanning tree, the algorithm is improved by combining the small root heap and disjoint set union, which significantly improves the operation efficiency. In the process of clustering, the divisive hierarchical clustering based on the minimum spanning tree is adopted, according to the principle of “nearest neighbor”, which ensures a better clustering effect and reduces the amount of calculation. Through the analysis of time complexity, this algorithm has a significant efficiency improvement compared with the previous algorithm. Finally, by using the data set of shared-bikes, and through Python simulation experiments, the model is effective in clustering quality.
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Yuan, X., Lu, Y. (2023). Improvement of Hierarchical Clustering Based on Dynamic Time Wrap**. In: Deng, Z. (eds) Proceedings of 2023 Chinese Intelligent Automation Conference. CIAC 2023. Lecture Notes in Electrical Engineering, vol 1082. Springer, Singapore. https://doi.org/10.1007/978-981-99-6187-0_65
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DOI: https://doi.org/10.1007/978-981-99-6187-0_65
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