scikit-learn(project中用的相对较多的模型介绍):2.3. Clustering(可用于特征的无监督降维)
參考:http://scikit-learn.org/stable/modules/clustering.html
在实际项目中,我们真的非常少用到那些简单的模型,比方LR、kNN、NB等。尽管经典,但在project中确实不有用。
今天我们不关注详细的模型,而关注无监督的聚类方法。
之所以关注无监督聚类方法。是由于。在实际项目中,我们除了使用PCA等方法降维外。有时候我们也会考虑使用聚类的方法降维特征。
Overview of clustering methods:
A comparison of the clustering algorithms in scikit-learn
Method name | Parameters | Scalability | Usecase | Geometry (metric used) |
---|---|---|---|---|
K-Means | number of clusters |
Very large n_samples, medium n_clusterswith MiniBatch code |
General-purpose, even cluster size, flat geometry, not too many clusters | Distances between points |
Affinity propagation | damping, sample preference | Not scalable with n_samples | Many clusters, uneven cluster size, non-flat geometry | Graph distance (e.g. nearest-neighbor graph) |
Mean-shift | bandwidth | Not scalable withn_samples | Many clusters, uneven cluster size, non-flat geometry | Distances between points |
Spectral clustering | number of clusters | Medium n_samples, small n_clusters | Few clusters, even cluster size, non-flat geometry | Graph distance (e.g. nearest-neighbor graph) |
Ward hierarchical clustering | number of clusters | Large n_samples andn_clusters | Many clusters, possibly connectivity constraints | Distances between points |
Agglomerative clustering | number of clusters, linkage type, distance | Large n_samples andn_clusters | Many clusters, possibly connectivity constraints, non Euclidean distances | Any pairwise distance |
DBSCAN | neighborhood size | Very large n_samples, medium n_clusters | Non-flat geometry, uneven cluster sizes | Distances between nearest points |
Gaussian mixtures | many | Not scalable | Flat geometry, good for density estimation | Mahalanobis distances to centers |
Birch | branching factor, threshold, optional global clusterer. | Large n_clusters andn_samples | Large dataset, outlier removal, data reduction. |
Euclidean distance between points |
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