Crate linfa_clustering
source · [−]Expand description
linfa-clustering aims to provide pure Rust implementations
of popular clustering algorithms.
The big picture
linfa-clustering is a crate in the linfa ecosystem, a wider effort to
bootstrap a toolkit for classical Machine Learning implemented in pure Rust,
kin in spirit to Python’s scikit-learn.
You can find a roadmap (and a selection of good first issues) here - contributors are more than welcome!
Current state
Right now linfa-clustering provides the following clustering algorithms:
Implementation choices, algorithmic details and tutorials can be found in the page dedicated to the specific algorithms.
Structs
DBSCAN (Density-based Spatial Clustering of Applications with Noise)
clusters together neighbouring points, while points in sparse regions are labelled
as noise. Since points may be part of a cluster or noise the transform method returns
Array1<Option<usize>>. It should be noted that some “border” points may technically
belong to more than one cluster but, since the transform function returns only one
label per point (if any), then only one cluster is chosen arbitrarily for those points.
Helper struct for building a set of Approximated DBSCAN hyperparameters
The set of hyperparameters that can be specified for the execution of the Approximated DBSCAN algorithm.
DBSCAN (Density-based Spatial Clustering of Applications with Noise)
clusters together points which are close together with enough neighbors
labelled points which are sparsely neighbored as noise. As points may be
part of a cluster or noise the predict method returns
Array1<Option<usize>>
Helper struct for building a set of DBSCAN hyperparameters
The set of hyperparameters that can be specified for the execution of the DBSCAN algorithm.
Gaussian Mixture Model (GMM) aims at clustering a dataset by finding normally distributed sub datasets (hence the Gaussian Mixture name) .
The set of hyperparameters that can be specified for the execution of the GMM algorithm.
The set of hyperparameters that can be specified for the execution of the GMM algorithm.
K-means clustering aims to partition a set of unlabeled observations into clusters, where each observation belongs to the cluster with the nearest mean.
An helper struct used to construct a set of valid hyperparameters for the K-means algorithm (using the builder pattern).
The set of hyperparameters that can be specified for the execution of the K-means algorithm.
OPTICS (Ordering Points To Identify Clustering Structure) is a clustering algorithm that doesn’t explicitly cluster the data but instead creates an “augmented ordering” of the dataset representing it’s density-based clustering structure. This ordering contains information which is equivalent to the density-based clusterings and can then be used for automatic and interactive cluster analysis.
The analysis from running OPTICS on a dataset, this allows you iterate over the data points and access their core and reachability distances. The ordering of the points also doesn’t match that of the dataset instead ordering based on the clustering structure worked out during analysis.
The set of hyperparameters that can be specified for the execution of the OPTICS algorithm.
This struct represents a data point in the dataset with it’s associated distances obtained from the OPTICS analysis
Enums
A specifier for the type of the relation between components’ covariances.
An error when modeling a GMM algorithm
A specifier for the method used for the initialization of the fitting algorithm of GMM
An error when modeling a KMeans algorithm
Specifies centroid initialization algorithm for KMeans.
An error when fitting with an invalid hyperparameter
An error when performing OPTICS Analysis