## 2 ML Methodologies
### Semisupervised Learning
#### 2.3.2 Semisupervised Clustering  
Semisupervised clustering is a powerful approach that combines labeled and unlabeled data to enhance clustering accuracy and interpretability. By incorporating labeled information, this technique guides the clustering algorithm to form more meaningful and accurate clusters. Labeled data provide explicit class labels, acting as a valuable anchor in the clustering process. Simultaneously, unlabeled data capture the underlying structure and patterns in the dataset, allowing for noise reduction and improved clustering outcomes. Various techniques, such as constrained clustering and co-training, are employed to effectively integrate labeled and unlabeled data. Constrained clustering leverages the constraints derived from labeled data to guide the clustering process. Co-training utilizes multiple models trained on different subsets of features or data, leveraging their predictions to label the unlabeled instances. By leveraging both labeled and unlabeled data, semisupervised clustering offers several benefits. It enhances the accuracy and interpretability of clustering results, providing a more comprehensive understanding of the underlying data structure. This facilitates insightful decision making and enables researchers and practitioners to gain valuable insights from their data. Overall, semisupervised clustering is a valuable technique for exploring complex datasets and extracting meaningful patterns [157]. Table 16 offers a brief overview of semi-supervised clustering algorithms. These algorithms are fundamental in the field of clustering, helping to improve clustering accuracy in scenarios involving partially labeled data. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Ref.** & **Description** & **Applications** & **Ref.** \\ \hline \multirow{3}{*}{Self-training} & \multirow{3}{*}{[158]} & \multirow{3}{*}{Self-training} & \multicolumn{2}{c}{Utilizes unlabeled water quality data to improve water quality prediction models.} & Water quality prediction, sensor data enhancement. & [164] \\  & & & Leverages data from multiple hydrological sensors to enhance streamflow forecasting accuracy. & Hydrological modeling, streamflow prediction. & [165] \\  & & & Combines diverse water quality data sources (e.g., remote sensing and in situ measurements) for more comprehensive assessments. & Water quality assessment, pollution source tracking. & [166] \\  & & & Generates synthetic environmental data environmental impact environmental assessment & Environmental impact assessment, sensor data analysis. & [167] \\  & & & Utilizes graph-based representations to model hydrological networks and optimize water resource allocation. & Hydrological network analysis, water allocation optimization. & [168] \\ \hline \multirow{3}{*}{Machine learning and environmental monitoring} & \multirow{3}{*}{Transductive support vector machines (TSVMs)} & \multicolumn{2}{c}{Labels data points based on their relationships with labeled instances, aiding in anomaly detection.} & Environmental anomaly detection, sensor data analysis. & [169] \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Semisupervised classification algorithms.