Scikit-learn：聚类clustering

http://blog.csdn.net/pipisorry/article/details/53185758

不同聚类效果比较

sklearn不同聚类示例比较

A comparison of the clustering algorithms in scikit-learn

不同聚类综述

Method name	Parameters	Scalability	Usecase	Geometry (metric used)
K-Means	number of clusters	Very large n_samples, medium n_clusters withMiniBatch 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 with n_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 and n_clusters	Many clusters, possibly connectivity constraints	Distances between points
Agglomerative clustering	number of clusters, linkage type, distance	Large n_samples and n_clusters	Many clusters, possibly connectivity constraints, non Euclideandistances	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 and n_samples	Large dataset, outlier removal, data reduction.	Euclidean distance between points

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DBSCAN聚类

代码示例

def Dist(x, y):
    from geopy import distance
    return distance.vincenty(x, y).meters

import pickle, subprocess, pwd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN

df = pd.read_pickle(os.path.join(CWD, 'middlewares/df.pkl'))

ll = df[['longitude', 'latitude']].values
x, y = ll[:, 0], ll[:, 1]

print('starting dbsan...')

dbscaner = DBSCAN(eps=DBSCAN_R, min_samples=DBSCAN_MIN_S, metric=Dist, n_jobs=-1).fit(ll)
pickle.dump(dbscaner, open(os.path.join(CWD, 'middlewares/dbscaner.pkl'), 'wb'))
print('dbsan dumping end...')

dbscaner = pickle.load( open(os.path.join(CWD, 'middlewares/dbscaner.pkl'), 'rb'))labels = dbscaner.labels_ # print(set(labels))colors = plt.cm.Spectral(np.linspace( 0, 1, len( set(labels)))) for k, col in zip( set(labels), colors) : marker = '.' if k == - 1 : col = 'k' marker = 'x' inds_k = labels == k plt.scatter(x[inds_k], y[inds_k], marker =marker, color =col) if pwd.getpwuid(os.geteuid()).pw_name == 'piting' : plt.savefig( './1.png') elif pwd.getpwuid(os.geteuid()).pw_name == 'pipi' : plt.show()

[DBSCAN]

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from: sklearn：聚类clustering

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