Evolving Cauchy possibilistic clustering for monitoring, detection and control of systems. In the lecture the different evolving algorithms will be presented and explained. This algorithms employs typicality to cluster as a measure of similarity, which is no more relative membership value, but an absolute value.. This has certain advantages, but also some drawbacks. The evolving and recursive nature of the algorithm is suitable to solve the problems of big-data. These algorithms evolves the structure during operation by adding and removing the clusters. This approach allows the identification of very different structures in size and shape and can cope with outliers.