Abstract
With the development of massive medical data, clustering algorithm becomes an effective way for medical data processing and data mining. On the one hand, it helps medical learners find effective information patterns from massive data; on the other hand, it promotes the development of medical technology and increase productivity. For traditional clustering algorithm, a single clustering index is difficult to meet people's needs of diversity and comprehensiveness. In contrast, multi-objective clustering (MOC) considers multiple objectives at the same time, and comprehensively deals with various clustering problems and standards, such as compactness, diversity of feature selection and high data dimension. Artificial bee colony algorithm (ABC) has a faster speed and embodies the idea of swarm intelligence. It imitates the optimization process of bees, and finally obtains the global optimal value. On this basis, this paper proposed a multi-objective artificial bee colony clustering algorithm (MOC-NABC) that is combined with current better-performed clustering algorithm. It takes normalized mutual information (NMI), Calinski-Harabasz (CH), Fowlkes-Mallows index (FMI) and silhouette coefficient (SC) of clustering as the final evaluation indexes. The experiment on UCI mouse protein gene dataset shows that the overall performance effect is greatly improved, e.g. compact clustering and the effective utilization of data features.
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Acknowledgement
This study is supported Natural Science Foundation of Guangdong (2020A1515010749, 2022A1515012077), Guangdong Province Innovation Team “Intelligent Management and Interdisciplinary Innovation” (2021WCXTD002), Shenzhen Higher Education Support Plan (20200826144104001).
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Chen, S., Tan, Y., Guo, J., He, Y., Geng, S. (2023). Medical Data Clustering Based on Multi-objective Clustering Algorithm. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_30
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