# clustlearn 1.0.0

New features

- New `kmeans()` function to perform K-Means clustering on a set observations and (if selected) see the step-by-step clustering procedure;
- New `dbscan()` function to perform DBSCAN clustering on a set observations and (if selected) see the step-by-step clustering procedure;
- New `gaussian_mixture()` function to perform Gaussian Mixture Model with Expectation Maximization clustering on a set observations and (if selected) see the step-by-step clustering procedure;
- New `agglomerative_clustering()` function to perform Agglomerative Hierarchical Clustering clustering on a set observations and (if selected) see the step-by-step clustering procedure;
- New `divisive_clustering()` function to perform Divisive Hierarchical Clustering (Bisecting K-Means) clustering on a set observations and (if selected) see the step-by-step clustering procedure;
- New `db1` data.frame with 500 observations (2 features) in the shape of two concentric circumferences;
- New `db2` data.frame with 500 observations (2 features) in the shape of two rotated moons;
- New `db3` data.frame with 500 observations (2 features) in the shape of three Gaussian distributions with varying parameters;
- New `db4` data.frame with 500 observations (2 features) in the shape of three elongated clusters;
- New `db5` data.frame with 500 observations (2 features) in the shape of three isotropic clusters;
- New `db6` data.frame with 500 observations (2 features) randomly distributed across the bi-dimensional plane.

Initial CRAN submission
