Research Article
A Generative Clustering Ensemble Model and Its Application in IoT Data Analysis
Table 4
NMI metrics of different approaches on IoT datasets.
| Datasets | Approaches | -means | SV | SWV | EM | CoCE | WCT | HFEC | CSPA | U-SPEC | DEC | IDEC | VaDE | GCE |
| KDD’99 | 0.1457 | 0.1968 | 0.2073 | 0.1964 | 0.3750 | 0.4273 | 0.4038 | 0.1638 | 0.3647 | 0.3204 | 0.3944 | 0.4164 | 0.4742 | NSL-KDD | 0.1686 | 0.2147 | 0.2566 | 0.2773 | 0.3905 | 0.4765 | 0.4164 | 0.2664 | 0.4367 | 0.4151 | 0.4633 | 0.4846 | 0.5402 | AWID | 0.2280 | 0.2661 | 0.2764 | 0.5051 | 0.3348 | 0.3927 | 0.5229 | 0.3562 | 0.6003 | 0.4471 | 0.4533 | 0.4462 | 0.6482 | UCI-IoT | 0.1517 | 0.1843 | 0.1856 | 0.2236 | 0.3146 | 0.2504 | 0.3359 | 0.2742 | 0.3057 | 0.3316 | 0.3513 | 0.3345 | 0.4217 | Synthetic dataset | 0.2509 | 0.3162 | 0.3865 | 0.3374 | 0.4285 | 0.4196 | 0.4957 | 0.4152 | 0.6960 | 0.4618 | 0.5176 | 0.5504 | 0.7160 |
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