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| Automatic Selection Method of Cluster Center Based on Positive Sequence Iterative Selection Strategy |
| WANG Wanliang1, LÜ Chuang1, ZHAO Yanwei1, GAO Nan1, YANG Xiaohan1, ZHANG Zhaojuan1 |
| 1.College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023 |
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Abstract The decision function of density peak clustering algorithm cannot determine the clustering center automatically and effectively. Therefore, a density peak clustering algorithm, automatically clustering by fast search and find of density peaks(AUTO-CFSFDP), is proposed. Firstly, the normalization process is carried out to make the uneven distribution of variables in the decision function become uniform. Secondly, the selection strategy based on positive-sequence iteration is presented to search elbow point according to the variation trend of the number of cluster core points in the process of determining the cluster center. A set of points before the elbow point is used as the cluster centers to complete clustering. Finally, the performance of AUTO-CFSFDP is evaluated on UCI datasets. AUTO-CFSFDP can cluster the datasets of arbitrary distributions without extra time consumption. The adaptability and clustering results are improved effectively.
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Received: 13 August 2018
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| Fund:Supported by National Natural Science Foundation of China(No.61572438,61702456,61873240) |
| About author:: (WANG Wanliang(Corresponding author), Ph.D., professor. His research interests include deep learning, artificial intelligence and big data.)(LÜ Chuang, master student. His research interests include big data and data mining.)(ZHAO Yanwei, Ph.D., professor. Her research interests include intelligent design and intelligent control.)(GAO Nan, Ph.D., lecturer. Her research interests include data mining, optimization analysis and bioinformatics.)(YANG Xiaohan, master student. Her research interests include big data and deep learning.)(ZHANG Zhaojuan, Ph.D. candidate. Her research interests include big data analysis, data-driven optimization and deep learning.) |
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2019, Vol. 32 
