Abstract
With the rapid development of cloud manufacturing (CMfg), a lot of cloud services are emerging on the Internet, which leads to cloud service clustering a critical topic. However, most existing approaches suffer from the low clustering quality due to the data sparsity condition, and are thus prone to the unreal result. To handle this problem, we put out a hybrid approach called HCA for cloud service clustering. At the first, we utilize Pearson Correlation Coefficient (PCC) and Proximity-Significance-Singularity (PSS) to compute the user similarity. Then, a similar group of users can be obtained using K-medoids algorithm, in which an ensemble model is established by incorporating those two user similarities. Based on two real-world data sets, the results show that the effectiveness of HCA.
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Acknowledgment
The author would like to appreciate the editors and experts for their greatful and helpful comments which encouraged to improve the quality of the paper. And, this paper was supported in part by National Key Research and Development Program of China under grant No. 2018YFB1703002, and in part by the Fundamental Research Funds for the Central Universities under grant No. 2019CDCGJX222.
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Liu, J., Chen, Y. (2020). A Hybrid Similarity-Aware Clustering Approach in Cloud Manufacturing Systems. In: Chien, CF., Qi, E., Dou, R. (eds) IE&EM 2019. Springer, Singapore. https://doi.org/10.1007/978-981-15-4530-6_11
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DOI: https://doi.org/10.1007/978-981-15-4530-6_11
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