Sentence examples for reasonable cluster from inspiring English sources

We used density peak clustering as primary streamline cluster algorithm, and Silhouette algorithm as the cluster validation algorithm to select reasonable cluster number, and the results of different clustering algorithms were compared.

The positive and negative update rule we introduced, represent the cooperative and hostile relationship, and the attribute configuration will convergence and one can identify the reasonable cluster configuration automatically.

Such an automatic algorithm can usually produce reasonable clustering results in some more universal propagation environments.

The work of Li et al. in [11] suggests a novel cluster identification algorithm, and it can produce reasonable clustering results in more universal propagation environments.

It is hard to assign early infected nodes to reasonable clusters solely based on the time line, since their time stamps of infection are too close.

In Figure 7, we observe that MLCD algorithm decrease the CHs number, and achieve a more reasonable clustering result than DCC algorithm in terms of local sensor density.

Monitoring the ACC as function of the cut-off value can reveal a threshold that improves the likelihood of obtaining a reasonable clustering performance when a network-based clustering algorithm was deployed.

This study provided an example of the successful application of model-based clustering to evaluate regional groundwater quality and demonstrated that better selection of the dimensional structure (i.e., selection of optimal variables and number of clusters) based on hydrochemistry was crucial in obtaining reasonable clustering results.

Therefore, we analyzed whether the ACC t) function can suggest a threshold within a given framework (i.e. a similarity measure and a clustering algorithm) that might lead to a reasonable clustering, even if a reference clustering set was not available.

Plots formed reasonable clusters within a sample site.

This is due to a more reasonable clustering of the observations.