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Acoustic Emission (AE) is a non-destructive testing known as assessment on damage detection in structural engineering. It also can be used to discriminate the different types of damage occurring in a composite materials. The main problem associated with the data analysis is the discrimination between the different AE sources and analysis of the AE signal in order to identify the most critical damage mechanism. Clustering analysis is a technique in which the set of object are assigned to a group called cluster. The objective of the cluster analysis is to separate a set of data into several classes that reflect the internal structure of data. In this paper was used k-means algorithm for partitioned clustering method, numerous effort have been made to improve the performance of application k-means clustering algorithm. This paper presents a current review on application clustering system in Acoustic Emission

A Preliminary Study Application Clustering System in Acoustic Emission Monitoring