def cluster_comparison_optics(self, radius, neighbors, visualize=False):
self.logger.info("Starting default optics Algorithm on All Points")
comparison_clustering = ComparisonClustering(self.data_frame)
comparison_clustering.cluster_optics(radius, neighbors)
if visualize and self.data_frame.dimensions == 2:
DataVisualization.visualize_plot_2d(
self.data_frame.df,
hue=comparison_clustering.clustering_name,
palette=DataVisualization.create_categorical_palette(
comparison_clustering.cluster_count))
def cluster_comparison_clique(self, intervals, threshold, visualize=False):
self.logger.info("Starting default clique Algorithm on All Points")
comparison_clustering = ComparisonClustering(self.data_frame)
comparison_clustering.cluster_clique(intervals, threshold)
if visualize and self.data_frame.dimensions == 2:
DataVisualization.visualize_plot_2d(
self.data_frame.df,
hue=comparison_clustering.clustering_name,
palette=DataVisualization.create_categorical_palette(
comparison_clustering.cluster_count))
def cluster_comparison_mean_shift(self, bandwidth=None, visualize=False):
self.logger.info("Starting default mean shift Algorithm on All Points")
comparison_clustering = ComparisonClustering(self.data_frame)
comparison_clustering.cluster_mean_shift(bandwidth)
if visualize and self.data_frame.dimensions == 2:
DataVisualization.visualize_plot_2d(
self.data_frame.df,
hue=comparison_clustering.clustering_name,
palette=DataVisualization.create_categorical_palette(
comparison_clustering.cluster_count))
def cluster_comparison_DBSCAN(self, eps, min_samples, visualize=False):
self.logger.info("Starting default DBSCAN Algorithm on All Points")
comparison_clustering = ComparisonClustering(self.data_frame)
comparison_clustering.cluster_DBSCAN(eps, min_samples)
if visualize and self.data_frame.dimensions == 2:
DataVisualization.visualize_plot_2d(
self.data_frame.df,
hue=comparison_clustering.clustering_name,
palette=DataVisualization.create_categorical_palette(
comparison_clustering.cluster_count))
def cluster_comparison_k_means(self, k, visualize=False):
self.logger.info('Starting default k-Means Algorithm on All Points')
comparison_clustering = ComparisonClustering(self.data_frame)
comparison_clustering.cluster_k_means(k)
if visualize and self.data_frame.dimensions == 2:
DataVisualization.visualize_plot_2d(
self.data_frame.df,
hue=comparison_clustering.clustering_name,
palette=DataVisualization.create_categorical_palette(
comparison_clustering.cluster_count))
def cluster_comparison_hierarchical_single_link(self,
cluster_count,
visualize=False):
self.logger.info(
"Starting default hierarchical single link Algorithm on All Points"
)
comparison_clustering = ComparisonClustering(self.data_frame)
comparison_clustering.cluster_hierarchical(cluster_count)
if visualize and self.data_frame.dimensions == 2:
DataVisualization.visualize_plot_2d(
self.data_frame.df,
hue=comparison_clustering.clustering_name,
palette=DataVisualization.create_categorical_palette(
comparison_clustering.cluster_count))