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The clustering of datasets has become a challenging issue in the field of big data analytics.
For example, Fig. 7 shows that the number of nodes in the bot cluster of dataset seven drops from 11 to 8, making the bot easier to identify.
At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples.
b Hierarchical clustering of dataset 4 Evenness profiles was performed based on correlation-based distance and visualized using dendrograms.
The results from k-way clustering of dataset 1 using k = 3, 4 and 5 are presented in Table 2.
a Hierarchical clustering of dataset 4 Diversity profiles was performed based on Euclidean distance and visualized using dendrograms.
Table 3 TOP 10 clusters of extended dataset Cluster Size Silhouette Label (TFIDF) Aver.
Table 1 TOP 10 clusters of core dataset Cluster Size Silhouette Labela Aver.
The resulting clusters of the dataset can be used for data modeling, outlier detection, discrimination analysis, and knowledge discovery.
Table 9 shows the number of removed edges and the number of detected clusters of this dataset.
Informally, the larger, the more likely it is that and will be the same, and hence the greater the degree of similarity between the clustering structure of dataset k and dataset.
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