Silhouette-based clustering using an immune network

Clustering is an important Data Mining task from the field of Knowledge Discovery in Databases. Many algorithms can perform clustering in a simple and efficient manner, but have drawbacks, such as the lack of a way to automatically determine the optimal number of clusters in the dataset and the possibility of getting stuck in local optima solutions. To try and reduce these drawbacks this work proposes a new clustering algorithm based on Artificial Immune Systems. This algorithm is characterized by the generation of multiple simultaneous high quality solutions in terms of the number of clusters in the database and the use of a cost function that explicitly evaluates the quality of clusters, minimizing the inconvenience of getting stuck in local optima solutions.