Exact(10)
Table 6 The clustering performances of different maximum layers using HSOM Layers of the HSOM 1 2 3 4 5 Average precision 0.47 0.53 0.53 0.53 0.53.
Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively.
Moreover, the experiments also demonstrate that the combinations of such weighting schemes and conventional hypergraph models can achieve competitive classification and clustering performances in comparison with some recent state-of-the-art algorithms.
In comparison with the existing state of the arts, our proposed approach not only achieves the advantage that the number of clusters can be automatically determined, but also the superior clustering performances on a range of temporal datasets, including synthetic dataset, time series benchmark, and real-world motion trajectory datasets.
The clustering performances were superior to those obtained using RNA expression.
A three cancer-type dataset was used to compare the clustering performances and time consumption between LRAcluster and iCluster+.
Similar(50)
We provide a clustering framework suitable to perform clustering and evaluate clustering performance on a large dataset.
While SC-BLSOM was performed using a normal PC server, a clustering performance was approximately 5% higher than that of the conventional BLSOM.
Shown in Table 1 is the microphone specific clustering performance.
Thus, selecting appropriate features affects clustering performance positively.
Clustering performance is mostly dependent on the text features' characteristics.
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