## 3 Unsupervised Machine Learning (Clustering)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & \
\begin{tabular}{c} **First** \\ **Author** \\ \end{tabular} & **Year** & **Model** &
\begin{tabular}{c} **Application** \\ **Field** \\ \end{tabular} & **Dataset** & **Accuracy** & **Details** \\ \hline
[42] & [PERSON] & 2020 & \begin{tabular}{c} The wavelet packet \\ transform (WPT) \\ \end{tabular} & \begin{tabular}{c} Real-time \\ monitoring for \\ railway axes \\ \end{tabular} & \begin{tabular}{c} Dataset from axles \\ with multiple \\ crack conditions \\ \end{tabular} & \
\begin{tabular}{c} Monitoring false \\ alarm rate is less \\ than 10\% \\ \end{tabular} &
\begin{tabular}{c} The WPT is \\ combined with SVM \\ diagnosis model \\ \end{tabular} \\ \hline
[45] & [PERSON] & 2020 & \begin{tabular}{c} Hybrid \\ intelligent generic \\ algorithm (HIGA) \\ \end{tabular} & \begin{tabular}{c} Crowd \\ monitoring in \\ smart cities \\ \end{tabular} & \
\begin{tabular}{c} WSN is used for a \\ dataset creation in \\ smart city \\ \end{tabular} &
\begin{tabular}{c} Exective crowd \\ monitoring in smart \\ cities using HIGA \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)change in color by a reaction among sensors and foreign materials. Hierarchical cluster algorithm is employed for separating the target materials. A common property of partition clustering algorithms is that all the clusters can be estimated at one time. Renowned partition clustering algorithms include k-means clustering algorithms and fuzzy c-means clustering algorithms. k-means clustering algorithms start with k clusters that are randomly generated. The center of a cluster is computed with the average of all the data points within that cluster. Subsequently each point is allocated to its closest cluster center. All the new centers of the clusters need to be evaluated again. These procedures are iterated till the pre-defined criterion is satisfied. Fuzzy clustering algorithms utilize the concept of fuzzy logic. Data point no longer needs to belong to a single cluster. Instead, the data point can belong to clusters to some degrees. This is the main difference for the fuzzy c-means algorithms and the k-means algorithms. Clustering algorithms have been employed in applications involving wireless sensor networks. A common difficulty is that the clustering process may be trapped in local minima. This can result in inaccurate cluster partitions. [PERSON] and [PERSON] [50] developed hybrid clustering techniques combined with evolutionary computing such that the global optima may be obtained. For the monitoring of outliers, a weight system was proposed, based on the volume and density of the data points. In this case, outliers are the ones with larger weights. There are many difficulties in the deployment of wireless underwater sensor networks (WUSNs), such as the high loss rate of transmission powers in the data transfer process. Clustering may address this issue by combining wireless sensors into cluster with local base station one hop away. As the sensor nodes are now close to the local base station, the transmitting power can be reduced significantly. [PERSON] et al. [51] proposed a novel k-means clustering scheme for local base station selection. It was found to be able to prolong the lifetime of WUSNs. The proposed algorithm can decide the optimal number of clusters in real time. The experimental results support that it can outperform the traditional clustering algorithms by more than 90%. A common property of the spectral clustering algorithm is the reduction in the dimension for the source data before measuring the similarity among the data. Shi-Malik algorithm is a spectral clustering algorithm which is popular in the segmentation process of images. [PERSON] and [PERSON] [52] developed a novel clustering algorithm to monitor the behavior pattern of the handling of ships. Ship information is obtained from the array of sensors, and then feeds into the identification system with trajectory data.