Scipy.cluster.hierarchy.fclusterdata +距离度量

Question

1)我正在使用scipy的hcluster模块。

所以我能控制的变量是阈值变量。我如何知道我的每个阈值的性能？也就是说，在Kmeans中，这种表现将是所有点到其质心的总和。当然，这必须进行调整，因为更多的簇通常=更小的距离。

对于这一点，我可以用hcluster做一些观察吗？

2)我意识到fclusterdata有很多可用的指标。我是基于关键术语的tf-idf对文本文档进行聚类。问题是，一些文档比其他文档更长，我认为余弦是“标准化”这个长度问题的好方法，因为文档越长，如果它们的内容是一致的，那么它在n维域中的“方向”应该保持不变。还有没有其他方法可以推荐呢？我如何评估？

Thx

Answer 1

可以计算集群中x的平均距离|x -集群中心|，就像K-means一样。下面的代码实现了这一暴力行为。(它必须是scipy.cluster或scipy.spatial.distance中的内置文件，但我也找不到它。)

在你的问题2上，通过。欢迎任何关于分层聚类的优秀教程的链接。

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#!/usr/bin/env python
""" cluster cities: pdist linkage fcluster plot
    util: clusters() avdist()
"""

from __future__ import division
import sys
import numpy as np
import scipy.cluster.hierarchy as hier  # $scipy/cluster/hierarchy.py
import scipy.spatial.distance as dist
import pylab as pl
from citiesin import citiesin  # 1000 US cities

__date__ = "27may 2010 denis"

def clusterlists(T):
    """ T = hier.fcluster( Z, t ) e.g. [a b a b a c]
        -> [ [0 2 4] [1 3] [5] ] sorted by len
    """
    clists = [ [] for j in range( max(T) + 1 )]
    for j, c in enumerate(T):
        clists[c].append( j )
    clists.sort( key=len, reverse=True )
    return clists[:-1]  # clip the []

def avdist( X, to=None ):
    """ av dist X vecs to "to", None: mean(X) """
    if to is None:
        to = np.mean( X, axis=0 )
    return np.mean( dist.cdist( X, [to] ))

#...............................................................................
Ndata = 100
method = "average"
t = 0
crit = "maxclust"
    # 'maxclust': Finds a minimum threshold `r` so that the cophenetic distance
    # between any two original observations in the same flat cluster
    # is no more than `r` and no more than `t` flat clusters are formed.
    # but t affects cluster sizes only weakly ?
    # t 25: [10, 9, 8, 7, 6
    # t 20: [12, 11, 10, 9, 7
plot = 0
seed = 1

exec "\n".join( sys.argv[1:] )  # Ndata= t= ...
np.random.seed(seed)
np.set_printoptions( 2, threshold=100, edgeitems=10, suppress=True )  # .2f
me = __file__.split('/') [-1]

    # biggest US cities --
cities = np.array( citiesin( n=Ndata )[0] )  # N,2

if t == 0:  t = Ndata // 4

#...............................................................................
print "# %s  Ndata=%d  t=%d  method=%s  crit=%s " % (me, Ndata, t, method, crit)

Y = dist.pdist( cities )  # n*(n-1) / 2
Z = hier.linkage( Y, method )  # n-1
T = hier.fcluster( Z, t, criterion=crit )  # n

clusters = clusterlists(T)
print "cluster sizes:", map( len, clusters )
print "# average distance to centre in the biggest clusters:"
for c in clusters:
    if len(c) < len(clusters[0]) // 3:  break
    cit = cities[c].T
    print "%.2g %s" % (avdist(cit.T), cit)
    if plot:
        pl.plot( cit[0], cit[1] )

if plot:
    pl.title( "scipy.cluster.hierarchy of %d US cities, %s t=%d" % (
        Ndata, crit, t) )
    pl.grid(False)
    if plot >= 2:
        pl.savefig( "cities-%d-%d.png" % (Ndata, t), dpi=80 )
    pl.show()