# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 3 Machine-Learning-Based Methodology for AE Testing
### Clustering Techniques  
Clustering techniques are based on unsupervised algorithms that work on data but without knowing the class they belong to. All clustering techniques are based on distance measurements between all the elements that are part of the dataset to be analyzed [33]. The elements that are more like each other will end up in the same cluster, that is, in the same group, whereas the less similar elements will end up in different clusters. Clustering techniques can be mainly used for two different purposes. The first is understanding: by observing the clusters that are formed from a dataset, it is possible to trace any relationships and patterns between the data themselves. The other purpose for which clustering can be used is the reduction of a dataset with too many elements to a smaller one [34]. This may be necessary, for example, to train faster a classification algorithm that had too large training set. By applying a clustering algorithm, it will be possible, in fact, to find which are its main clusters, such as those subsets of data that are like each other, taking only some of them, which are the most representative for each cluster [35]. Clustering algorithms can be grouped into two large families:  
* Aggregative or bottom-up methods: Initially, all the elements of the dataset are identified as separate clusters. The elements closest to each other are then merged into a single cluster, thus aggregating smaller clusters into larger clusters. All this is carried out until a predetermined condition is reached, which could be the number of clusters, a minimum distance between clusters or other, depending on the algorithm used. * Divisive or top-down methods: It starts from a single large cluster that contains all the elements. Subsequently, the cluster is divided into smaller and smaller clusters. Proceed until a stop condition is reached, which is usually the desired number of clusters. There are also other subdivisions of clustering algorithms. One of these sees the contrast between exclusive and nonexclusive clustering. In exclusive clustering, an element can belong to one and only one cluster. In nonexclusive clustering, also known as fuzzy clustering, an element can belong to several clusters at the same time, to each according to a probability p. Obviously, the sum of the probabilities of belonging of an element to the various clusters must be equal to one [36]. Fuzzy c-means clustering iteratively searches for a set of fuzzy clusters and associated cluster centers that characterize the data structure in the best possible way [37]. The user specifies the number of clusters to locate in the dataset to be grouped. [PERSON] et al. [38] applied fuzzy c-means (FCM) techniques to classify the acoustic emission (AE) signal into different signal sources. The authors performed the test using a pulse, pencil, and spark signal source on the surface of the solid block of steel. Using the AET 5000 system, they measured four parameters: event duration, peak width, rise time, and loop back count. [PERSON] et al. [39] investigated local damage in composite materials based on the analysis of acoustic emission (AE) signals. The authors applied fuzzy C-means clustering techniques associated with principal component analysis to analyze AE data clusters and subsequently correlate them to material damage mechanisms. Continuous and discrete wavelet transforms are applied to typical AE signal damage mechanisms. [PERSON] et al. [40] adopted fuzzy C-means clustering associated with a principal component analysis to detect damage in glass-polyester composites with the AE technique. [PERSON] et al. [41] evaluated the damage of concrete structures subjected to pure torsional load by proposing a method based on AE and kernel fuzzy c-means. Time and frequency domain signals were used to classify the damage. [PERSON] et al. [42] studied the damage mechanisms in standard open-hole tensile (OHT) laminated composites through the AE.