# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Unsupervised Learning in Network Measurement  
Unsupervised clustering methods were researched before the development of deep learning and are still in use today [54]. In the real world or a real network environment, it could be difficult or expensive to manually label some data due to a lack of appropriate prior knowledge or because it would be too expensive. Unsupervised learning is now a technique used by computers to solve various pattern recognition issues based on training samples of unknown categories. It does not process supervised signals; it merely processes the so-called \"features\". Unsupervised learning is used to train models to learn the structure of datasets, give users useful information about new samples, and assist managers in making the best decisions possible using both qualitative and quantitative methods. This process calls for a methodical learning process that represents precise input signals in a way that reveals the structure of the entire set of input signals [55]. The unsupervised learning algorithm has three main characteristics:  
* Unsupervised learning has no clear purpose;
* Unsupervised learning does not need to label data;
* Unsupervised learning cannot quantify the effect. Therefore, it is difficult to determine whether a job has been finished during unsupervised learning using this strategy. Unsupervised learning lacks definite markers that can be used to determine whether a goal has been attained. Currently, clustering and dimension reduction are two widely utilized unsupervised algorithms that are mostly used in the following scenarios:  
1. One of the most common tasks in unsupervised learning is clustering. Instead of defining groups before seeing data, it enables us to identify and evaluate naturally \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Ref** & **Application** & **Dataset** & **RF** & **ANN** & **SVM** & **FNN** & **Else** \\ \hline
[22] & Predict Packet loss & 10 DTN system in NERSC & 97-99\% & - & - & - & - \\ \hline
[23] & Predict throughput & Real Dataset & 91\% & 93\% & 89\% & - & XGBoost93\% \\ \hline
[26] & Predict Bandwidth & Manual Dataset & - & - & - & 78\% & - \\ \hline
[51] & Predict Path Delay & Commercial mobile operators & 73\% & - & 66\% & - & LR:60\% \\ \hline
[52] & Predict Path loss & Real Dataset & - & - & 85\% & - & RBF:89\% \\ \hline
[53] & Congestion control & Manual Dataset & - & - & 98\% & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Network Measurement Application and ML Algorithm AnalysisPrediction accuracy of different supervised ML algorithms. generated groups, that is, groups that were established depending on the data itself. K-Means, hierarchical, and probabilistic clustering are popular methods;
2. Too many features could waste an ML model's storage space and processing time in the event of a dimensional catastrophe. Researchers want to represent data accurately in smaller dimensions without sacrificing too much useful information. The dimensions of the data can be decreased using a dimension-reduction technique. PCA and SVD were the two basic methods employed. K-Means in Network Measurement  
When the datasets are segregated from one another, K-Means is a quick, reliable, and simple algorithm that can produce accurate results. This algorithm groups data points into clusters to minimize the square distance between each data point and the center of mass [19,56]. Its major job is to automatically group samples that are similar into categories. The key concept is to randomly select K objects to serve as the initial cluster centers, divide the data into K groups, measure the distance between each seed cluster center and each item, and then assign each object to the cluster center that is closest to it. Up until the termination condition was met, this process was repeated. [PERSON] et al. [23] suggested a machine learning (ML)-based automatic decision system to substitute expert users. To dynamically choose the optimum device to measure the available bandwidth, four ML algorithms were utilized.