## 5 Experimental Results and Discussion
### UCI Text Set Document Clustering  
The TTC-3600 data set is a collection of 3600 news documents from six well-known news portals and agencies in Turkey from May to July 2015, which cover six different categories of economics,  
Figure 2: Flow chart of user grouping in a Microblog network based on SSC.  
culture, arts, health, politics, sports, and technology [34]. First, the data was preprocessed, which included deleting irrelevant data such as JavaScript, HTML tags, punctuation symbols and operators, and removing stop words. Then, the word stem was extracted based on the Zemberek open-source NLP toolkit. The data consisted of 3600 samples, and a total of 5693 feature words were extracted. At last, the vector space model (VSM) and TF-IDF function to represent the document were combined as:  
\[w_{ki}=\frac{tf_{ki}\times log(N/n_{k})}{\sqrt{\sum_{k=1}^{n}(tf_{ki}\times log( N/n_{k}))^{2}}}. \tag{21}\]  
In Equation (21), \(t_{k}\) is the \(k\)th feature word appearing in the document \(d_{i}\), and \(tf_{ki}\) is the frequency in \(t_{k}\), \(d_{i}\). \(n_{k}\) is the number of documents containing \(t_{k}\) in the document set, and \(N\) is the total number of documents. The text sparseness of vector representation is high. And the high dimension will reduce the efficiency of the algorithm. Feature selection before the SSC algorithm is implemented. Usual feature selection methods include feature selection based on association rules and principal component analysis (PCA). In this work, PCA is used to transform the matrix dimension from D\(\times\)n to r\(\times\)n, D = 5693, \(n\) = 3600. \(r\) is a new feature dimension, which is 30, 40, 50, 60, 100 respectively. The clustering results are evaluated by clustering indicators ACC, ARI, Precision, Recall and NMI [35]. The evaluation results are shown in Table 3.  
Experimental results showed that the SSC clustering effect was greatly affected by the data dimension, and the low-dimension representation led to the loss of effective information. With the increase of dimension, the noise and outlier data points would also increase correspondingly. The SSC clustering effect was the best when r was 50 in the data set. The algorithm performed with good accuracy for the data set with appropriate dimension reduction. Compared with the recent literature, the clustering effect of the proposed classification method was better than those of SVM and KNN algorithms [34]. As we know, dimensions of the samples usually have a negative effect in the pattern recognition problem of clustering. In this work, the principal component analysis (PCA) was used to reduce dimensionality. In fact, the suitable dimensionality representation was an open problem. In [34], the step of dimension reduction was not included in the SVM and KNN methods, so the proposed classification method was better than those of SVM and KNN algorithms.  
Experimental results showed that the SSC clustering effect was greatly affected by the data dimension, and the low dimension representation led to the loss of effective information. With the increase of dimension, the noise and outlier data points would also increase correspondingly. The SSC clustering effect was the best when r was 50 in the data set. The algorithm performed with good accuracy for the data set with appropriate dimension reduction. Compared with the recent literature, the clustering effect of the proposed classification method was better than those of SVM and KNN algorithms [34].