Menu
Is there any kind of subspace clustering packages available in scikit-learn.
417
Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. Often in high dimensional data, many dimensions are irrelevant and can mask existing clusters in noisy data.
It stands for “Density-based spatial clustering of applications with noise”. This algorithm is based on the intuitive notion of “clusters” & “noise” that clusters are dense regions of the lower density in the data space, separated by lower density regions of data points. Scikit-learn have sklearn. cluster.
DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density. Read more in the User Guide.
Agglomerative clustering but for features instead of samples. Hierarchical clustering with ward linkage. Fit the hierarchical clustering from features, or distance matrix. Fit and return the result of each sample's clustering assignment.
If someone is still interested. Yes, there is one that uses scikit-learn: Subspace-Clustering. It is a toolbox for large scale subspace clustering. The clustering algorithms are implemented as two classes ElasticNetSubspaceClustering and SparseSubspaceClusteringOMP that have a fit function to learn the clusters. They may be used in the same way as the KMeans, SpectralClustering and others that are in sklearn.cluster.
126
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
