Algorithms for clustering data
Algorithms for clustering data
Automatically Determining the Number of Clusters in Unlabeled Data Sets
IEEE Transactions on Knowledge and Data Engineering
| Hi-index | 0.00 |
Determining number of clusters present in a data set is an important problem in clustering. There exist very few techniques that can solve this problem satisfactorily. Most of these techniques are expensive with regard to computation time. This paper proposes an alternative solution for the concerned problem that makes use of the concepts of genetic algorithms, the PBM cluster validity index and a recently developed visual mechanism for determining the clustering tendency (VAT, Visual Assessment of Tendency for clustering). It is shown that the present approach is able to find the appropriate number of clusters very efficiently.