An iterative approach for clustering optimization and validation

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Clustering is a common technique, in all areas where information is obtained from the collected data. In this work, three well-known clustering algorithms namely, K-means, Spectral and DBSCAN are investigated in terms of their validity using four clustering validity indexes, Rand, Adjusted Rand, Jaccard, Silhouette. These clustering algorithms are applied on three data sets which have different characteristics. Thus steps have been taken for an automated clustering optimization system.TÜBİTAK ; European Commissio