The amount of data being collected in engineering is increasing exponentially and it is no longer practical to rely on traditional manual methods to analyse these data. Clustering, which automatically finds natural groups in the data, is an important data exploration technique. It has many applications in different areas of engineering, including engineering design, manufacturing system design, quality assurance, production planning and process control. Many clustering algorithms have been proposed from different research disciplines. However, efforts to perform effective and efficient clustering on large data sets only started in recent years with the emergence of data mining. This paper provides a review of various clustering algorithms in data mining and describes a number of important engineering applications of these algorithms.

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