Mini-Batch K-Means
Mini-Batch K-Means is a modified version of k-means that makes updates to the cluster centroids using mini-batches of samples rather than the entire dataset, which can make it faster for large datasets, and perhaps more robust to statistical noise.
Mean Shift
Mean shift clustering involves finding and adapting centroids based on the density of examples in the feature space.
OPTICS
OPTICS clustering (where OPTICS is short for Ordering Points To Identify the Clustering Structure) is a modified version of DBSCAN.
Spectral Clustering
Spectral Clustering is a general class of clustering methods, drawn from linear algebra. to tune is the n_clusters hyperparameter used to specify the estimated number of clusters in the data.
Gaussian Mixture Model
A Gaussian mixture model summarizes a multivariate probability density function with a mixture of Gaussian probability distributions.
sagnik1511
commented
2 years ago
This PR fixes for issue #32
Changes made
Added 5 new clustering models in models.py
Reason
Mini-Batch K-Means Mini-Batch K-Means is a modified version of k-means that makes updates to the cluster centroids using mini-batches of samples rather than the entire dataset, which can make it faster for large datasets, and perhaps more robust to statistical noise. Mean Shift Mean shift clustering involves finding and adapting centroids based on the density of examples in the feature space. OPTICS OPTICS clustering (where OPTICS is short for Ordering Points To Identify the Clustering Structure) is a modified version of DBSCAN. Spectral Clustering Spectral Clustering is a general class of clustering methods, drawn from linear algebra. to tune is the n_clusters hyperparameter used to specify the estimated number of clusters in the data. Gaussian Mixture Model A Gaussian mixture model summarizes a multivariate probability density function with a mixture of Gaussian probability distributions.