A Hybrid Similarity-Aware Clustering Approach in Cloud Manufacturing Systems
| Autor: | Youling Chen, Jian Liu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Ensemble forecasting
Computer science business.industry media_common.quotation_subject Cloud computing computer.software_genre Pearson product-moment correlation coefficient symbols.namesake Similarity (network science) symbols Quality (business) The Internet Data mining Cloud manufacturing business Cluster analysis computer media_common |
| Zdroj: | IE&EM 2019 ISBN: 9789811545290 |
| DOI: | 10.1007/978-981-15-4530-6_11 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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