CLASSIX API Reference

classix.CLASSIX([sorting, radius, minPts, ...])	CLASSIX: Fast and explainable clustering based on sorting.
classix.CLASSIX.fit_transform(data)	Cluster the data and return the associated cluster labels.
classix.CLASSIX.fit(data)	Cluster the data and return the associated cluster labels.
classix.CLASSIX.predict(data[, memory])	Allocate the data to their nearest clusters.
classix.CLASSIX.explain([index1, index2, ...])	'self.explain(object/index) # prints an explanation for why a point object1 is in its cluster (or an outlier) 'self.explain(object1/index1, object2/index2) # prints an explanation why object1 and object2 are either in the same or distinct clusters
classix.CLASSIX.explain_viz([showalldata, ...])	Visualize the starting point and data points
classix.CLASSIX.visualize_linkage([scale, ...])	Visualize the linkage in the distance clustering.
classix.CLASSIX.load_cluster_centers()	Load cluster centers.
classix.CLASSIX.load_group_centers()	Load group centers.
classix.CLASSIX.gcIndices(ids)
classix.CLASSIX.form_starting_point_clusters_table([...])	form the columns details for group centers and clusters information
classix.CLASSIX.calculate_group_centers(...)	Compute data center for each label according to label sequence.
classix.CLASSIX.outlier_filter([...])	Filter outliers in terms of min_samples or min_samples_rate.
classix.CLASSIX.pprint_format(items[, truncate])	Format item value for clusters.
classix.CLASSIX.reassign_labels(labels)	Renumber the labels to 0, 1, 2, 3, ...
classix.CLASSIX.gc2ind(spid)