Review of Clustering Techniques in Control System - Singh, Saumya; Srivastava, Smriti (Procedia computer science, 2020)
Data clustering is an important tool in data mining, that helps to retrieve useful data from large amount of available data. In this digital era data is available in abundance, but finding useful data has become a challenging task. For this, data clustering is an effective and common approach where we can group data by seeing some pattern or inherent data similarity in one group. Clustering is an unsupervised learning method of linearly separable and nonlinearly separable clusters widely used for different nature of application [1]. Data clustering finds application in classification of patterns in different areas such as artificial intelligence, summarization, learning, segmentation, speech recognition, pattern recognition, image segmentation, biology, marketing, data mining, modelling and system identification etc [5][24][25]. No one clustering technique can be said as best or better than other, because different clustering algorithms co-exists and are application specific. This paper majorly emphasises on critical review of clustering algorithms used in control systems, but a brief overview is also given about all major algorithms.