An efficient algorithm based on cluster analysis for exploring structure of large multivariate datasets.

This study provides the cluster and factor analyses of 40 species of Teucrium, which is a plant and belongs to the Lamiaceae family with more than 260 species distributing all around the world under their 21 micromorphology characters are performed. In establishing the optimal a number of clusters in the K-means algorithm, we use a popular clustering index called silhouette index. By combining factor analysis with the clustering methods based on the silhouette validation measure, we have developed an efficient algorithm for clustering large multivariate datasets using Mathematica software, which is well-known for its algebraic manipulation capabilities.