## 2 Related Work
That characteristic is not suitable for the phase I application of SPC since in a phase I dataset, most data are usually normal, and only a few are abnormal. In addition to the \(K\)-means algorithm, there are many advanced anomaly-detection algorithms in the literature [34; 35; 36]. However, these are semi-supervised algorithms whose performance is easily affected by outliers in the dataset. These algorithms usually assume that the dataset contains only clean data (either normal class or abnormal class). In addition, one-class SVM is also a commonly used anomaly-detection method, but its performance is easily affected by outliers [37; 38], so it is often used when the training dataset is not polluted by outliers. In the current problem domain, unsupervised algorithms are more suitable than other algorithms since the dataset obtained in phase I may consist of IC and OOC profiles. Furthermore, the type of collected profile is usually not known beforehand. [PERSON] et al. [39] pointed out that unsupervised algorithms need to determine an important parameter, called the contamination rate, which is equivalent to the proportion of abnormal data in the dataset. They also indicated that the main disadvantage of unsupervised learning is the unsupervised nature and the fact that the hyperparameters must be set appropriately to obtain satisfactory results. In previous studies, although researchers claimed to use unsupervised learning, they did not have a reasonable proposal for how to determine the contamination rate. [PERSON] et al. [20] even pointed out that there are some studies that refer to methods trained with pure normal training data as unsupervised methods. [PERSON] et al. [40] proposed an ensembled outlier-detection method for wireless sensor networks. They used a set of three well-knownunsupervised algorithms; however, they used a clean, labeled dataset to build the model. [PERSON] et al. [41] proposed a pruning method to determine the proportion of outliers in the dataset. Their method involves calculating an outlier coefficient for each sample in a dataset. An adjustment factor is used to determine the proportion of outliers, with this factor set according to the size and distribution of the dataset. [PERSON] et al. [42] proposed an ensemble-based anomaly-detection method. They claimed the proposed method can be applied in an unsupervised manner when labels do not exist. However, this method relies on a grid search to find the best hyperparameters that provide the highest accuracy. This means that the method needs to know the proper separation of inliers and outliers before implementation. [PERSON] et al. [39] compared many unsupervised outlier-detection methods, but the contamination rate was determined by trying a range of values. This approach is not feasible for phase I profile monitoring for the reasons stated above. It can be seen from the above literature review that we need an anomaly-detection method that can detect the abnormal data in a dataset in order to establish a clean dataset. Only in this way can we then use more advanced semi-supervised algorithms, such as one-class SVM [43,44] or deep SVDD algorithms [45], to improve the classification accuracy.