## 4 Dataset
### Experiment and Results  
Given our focus on traditional Chinese folk music, we followed the motivation presented at the beginning Section 4, i.e., to implement the concept of clustering symbolic music in pursuit of identifying the collected musical corpus genres through data mining. First, in the data pre-analysis section, we performed text-weighted pre-processing on the music dataset in TCPS MIDI format. The ethnicity of Chinese traditional folk music was analyzed and verified on this dataset. Then, in order to demonstrate the wide applicability of different clustering models in this dataset and the objectivity of clustering, we used three clustering methods to cluster the music data from three perspectives: division, density, and hierarchy. Among them, the k-value clustering determined by the division-based K-means clustering method achieves the smallest squared error, with dense clustering results and significant inter-class differences. However, the difficulty in choosing k-values or the sensitivity to noisy and isolated points may lead to numerous iterations and long time-consuming periods. Instead of explicitly generating clusters of data, OPTICS simply sorts the objects in the data object set and then computes an ordered list. It contains a lot of information for extracting clusters and is insensitive to the parameter transformations in the  
Figure 4: Seven internal or external metrics for evaluation of clustering algorithms. clustering process. However, they are not as good as K-means and Birch as far as clustering results are concerned. There is more computational efficiency in the Birch algorithm, which saves more memory and clusters better than OPTICS, as shown in the Table 1.  
In the Chinese seven-note scale, the scale composition is mainly composed of a pentatonic scale, including \(Gong,Shang,Jue,Zhi,Yu\), to which two of the other four partials \(Qingjiao,Bianzhi,Biangong,Run\) are added. The pitch name system is expressed as \(C,D,E,G,A,F,F\#,B,Bb\)[46]. There are three sources of Yayue in all dynasties. It was inherited from the court music works of the [PERSON] dynasty, reconstructed according to the music theory of the Zhou dynasty, or newly produced on the basis of new vocal and popular music. It is made up of the following elements: \(Gong,Shang,Jue,Bianzhi,Zhi,Yu\), and \(Biangong\)[47]. Qingyue is also known as Qingshang music. It is a traditional music that emerged in the Three Kingdoms, Jin Dynasty, Southern and Northern Dynasties and dominated the musical life of the time. It consists of \(Gong,Shang,Jue,Bianzhi,Zhi,Yu\), and \(Biangong\)[48]. Yanyue was a very artistic song and dance music that provided entertainment and appreciation during the palace banquets from the [PERSON], [PERSON] and [PERSON] dynasties. The court Yanyue of [PERSON] and [PERSON] dynasties reflects the highest achievement of music culture in this period. It comes from the continuous accumulation of traditional music of the Han nationality and the large-scale input of foreign music since the Han and Wei Dynasties. It is composed of \(Gong,Shang,Jue,Bianzhi,Zhi,Yu\), and \(Biangong\)[49]. We back-track the data to trace the components after clustering, and we go back to the first seven musical elements in each cluster, which correspond to the five main tones and four partial tones of traditional Chinese national music [50]. The results revealed that cluster 1 possessed the structural characteristics of the Yanyue mode, clusters 2, 3, 4, 8, and 9 has the structural characteristics of the Qingyue mode, and clusters 5, 6, and 7 features the structural characteristics of the Yayue mode, as shown in the Table 2.  
Figure 5 shows the three-dimensional visualization results of clustering under K-Means, OPTICS, and Birch. It can be observed that the figure matches the objective evaluation metrics in Table 3. k-Means and OPTICS demonstrate relatively better aggregation results. Similar results indicate that annotated text data are more suitable for partition and density-based clustering methods.