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 |