用于聚类混合数据的Scikit学习（数字和分类）

Question

有人可以帮助修改下面的工作示例，从共享数据创建集群吗？在

该示例使用Scikit Learn的均值偏移聚类来识别农艺设施中相似/共定位植物物种的斑块。在

在这类问题中，除了使用数值之外，还提出了类似的问题，但我认为这个例子不同的原因是：这个问题中的非数值不能简单地用一个和零个虚拟值编码。例如，我们不能一个热编码值，比如“Aristolochia macrophylla”和“Aristolochia durior'，因为名称上具有这种相似性的物种需要根据它们的科系进行聚类，除了X和Y值给出的地理接近性。在创建集群时，名称的相似性与位置同样重要。在

我尝试了两种方法：给物种名称中的字母指定任意的数值，以表明拼写相似的名称在数字线上的距离会更近。我打算对这些值应用自动缩放，并用X和Y坐标插入脚本。这行不通，因为不同的名字在数字上非常相似。在

我的另一个尝试是通过使用Levenstein距离来合并范畴值。但距离的输出仅基于比较两个值。如果输出显示每个字符串到所有其他字符串的距离，如何将结果实现为Meanshift算法的输入？在

总之，这里是目前只使用数值的数据和工作脚本。 我真的很感激任何关于如何使用分类值的相似性对这些数据进行聚类的例子。在

谢谢你

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn.cluster import MeanShift, estimate_bandwidth
from sklearn.datasets.samples_generator import make_blobs

df=pd.DataFrame()

df["POINT_X"]=[-75.933169765,-75.932900302,-75.933060039,-75.932456135,-75.932334122,-75.933383845,-75.933378563,-75.933290334,-75.933302506,-75.932024669,-75.931803297,-75.931777655,-75.9317845,-75.931807731,
               -75.931794839,-75.932045113,-75.932165473,-75.932763574,-75.93216276,-75.932066326,-75.931934871,-75.932294115,-75.931852284,-75.93187799,-75.932063549,-75.932377939,-75.932466697,-75.9324484,-75.932523695,
               -75.932484492,-75.931882652,-75.932006344,-75.932228988,-75.932702486,-75.933245229,-75.933165385,-75.932990797,-75.932741398,-75.932519195,-75.932336262,-75.932264764,-75.932953569,-75.932938167,-75.933098289,
               -75.932503985,-75.932597591,-75.932551382,-75.932541384,-75.932575066,-75.932751274,-75.932869969,-75.932086405,-75.932125915,-75.932089623,-75.932229816,-75.932356252,-75.93221234,-75.932505964,-75.932455199,
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               -75.933325084,-75.933336448,-75.934785616,-75.934843128,-75.93387422,-75.933996517,-75.934114484,-75.934560855,-75.935138185,-75.935228902,-75.935550248,-75.935326059,-75.935167468,-75.935038326,-75.934937151,
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               -75.934018133,-75.93397582,-75.933918536,-75.933971179,-75.933877169]

df["POINT_Y"]=[38.95259201,38.952468493,38.952585964,38.952220643,38.952172451,38.952978948,38.952611101,38.952620123,38.952527583,38.952013642,38.951971095,38.951950598,38.951878617,38.951867573,38.952051039,38.952319899,
               38.952751776,38.952261808,38.951645828,38.951591344,38.951583443,38.951660428,38.951750197,38.951752666,38.951776696,38.951792968,38.951787078,38.951862848,38.951800999,38.951744805,38.951870508,38.951889649,
               38.951936158,38.95170948,38.951751749,38.951735386,38.951742727,38.951588575,38.951528477,38.951520106,38.951519453,38.951936698,38.952010261,38.952013956,38.952102079,38.952165877,38.952146088,38.952089106,
               38.952117254,38.952151545,38.949969545,38.951201998,38.951159228,38.951123753,38.950778391,38.950531943,38.950989092,38.950097211,38.950208568,38.950065183,38.950071356,38.949923603,38.9498474,38.949809668,
               38.949757376,38.949571133,38.951447294,38.95147755,38.950581745,38.950733667,38.951069352,38.951237478,38.95107276,38.95096753,38.9508122,38.950734862,38.950688169,38.950514372,38.950075351,38.950010511,38.949960875,
               38.949992064,38.95007398,38.950101272,38.950295815,38.950227769,38.950211517,38.950441255,38.950335632,38.95024686,38.950307666,38.950528546,38.950513096,38.950187972,38.950217841,38.950263645,38.950510523,
               38.950755399,38.950708302,38.950286311,38.950229957,38.950164615,38.950045229,38.949970825,38.949877169,38.949993101,38.949660647,38.949543522,38.949625589,38.949412861,38.949487811,38.949880172,38.951839048,
               38.952063455,38.949880835,38.951913953,38.949897842,38.949754481,38.949913573,38.951052934,38.951134326,38.951215119,38.951281057,38.951294341,38.951397886,38.951533389,38.951672146,38.949658462,38.950068808,
               38.949883166,38.949852263,38.949919533,38.950057898,38.950028999,38.950188832,38.950304129,38.950435138,38.950514515,38.950622084,38.950381874,38.949994828,38.950052327,38.949830647,38.949824853,38.949732702,
               38.949761675,38.949791427,38.949879419,38.949914074,38.949955099,38.951691376,38.951766177,38.951785811,38.951832242,38.951733008,38.950873805,38.951440038,38.951405074,38.951254936,38.951212584,38.951201821,
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               38.948821237,38.948818421,38.948795076,38.949678178,38.949281509,38.949751466,38.949261269,38.949715525,38.949652229,38.949566304,38.949532396,38.949542936,38.949567821,38.94953658,38.949563742,38.948735942,
               38.952147575,38.952155751,38.951912912,38.951985954,38.952728799,38.952622921,38.952451597,38.952436249,38.95231594,38.952313127,38.951745893,38.952390373,38.952286187,38.952708734,38.951839413,38.952030386,
               38.951616852,38.951420298,38.951608998,38.952554863,38.9520134,38.951292914,38.951667791,38.952112184,38.954031241,38.953799626,38.953837241,38.953853864,38.953692287,38.953686947,38.953751245,38.953616457,
               38.95369262,38.953694331,38.953744736,38.953742862,38.953858308,38.953767308,38.953659111,38.953499777,38.953494864,38.953676808,38.953570088,38.953574927,38.953146008,38.953138966,38.953219752,38.953218684,
               38.953196026,38.953217491,38.953260642,38.953365184,38.953343071,38.953392347,38.95584336,38.955799692,38.956182326,38.95621302,38.956049617,38.957470088,38.957171152,38.956453402,38.956649954,38.956791692,
               38.957180989,38.957521592,38.955754158,38.95553646,38.955953035,38.956405511,38.956660878,38.957086511,38.957423389,38.957793854,38.957835976,38.955448024,38.955021013,38.954934154,38.954927544,38.954598007,
               38.954570833,38.954367294,38.954343,38.954497793,38.954471,38.954821256,38.954369125,38.955348715,38.955333171,38.955343991,38.955489753,38.955493927,38.955516735,38.955049181,38.955110383,38.954724398,38.954521524,
               38.954517463,38.954512208,38.954493542,38.954434212,38.954117479,38.95435162,38.954310712,38.954277052,38.954161078,38.954580606,38.954197375,38.955451505,38.955596079,38.955045523,38.955097295,38.955970146,
               38.954232335,38.95411988,38.953505553,38.955288869,38.955759644,38.955647996,38.955040953,38.954949777,38.95485026,38.954643337,38.954546745,38.953547289,38.953542137,38.953995634,38.954146947,38.954862356,
               38.953287566,38.954523419,38.954915863,38.955002144,38.954945777,38.955006524,38.95507815,38.955120243,38.953067979,38.953073084,38.953453648,38.953640022,38.953641026,38.954062633,38.954027667,38.954110137,
               38.954249401,38.953874232,38.953529725,38.953628972,38.953476826,38.95351151,38.953498365,38.953491846,38.953767787,38.953843351,38.953849161]

#Must incorporate these identifiers and cluster by similarity of species in addition to their proximity.
df["Category"]=['Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla',
                'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla',
                'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia macrophylla', 'Aristolochia durior', 'Aristolochia durior', 'Aristolochia durior', 'Aristolochia durior',
                'Aristolochia durior', 'Aristolochia durior', 'Aristolochia durior', 'Aristolochia durior', 'Aristolochia durior', 'Aristolochia durior', 'Aristolochia durior', 'Aristolochia durior', 'Aristolochia durior',
                'Aristolochia durior', 'Aristolochia durior', 'Aristolochia durior', 'Aristolochia tomentosa', 'Aristolochia tomentosa', 'Aristolochia tomentosa', 'Aristolochia tomentosa', 'Aristolochia tomentosa',
                'Aristolochia tomentosa', 'Aristolochia tomentosa', 'Aristolochia tomentosa', 'Aristolochia tomentosa', 'Aristolochia tomentosa', 'Aristolochia tomentosa', 'Aristolochia tomentosa', 'Aristolochia tomentosa',
                'Aristolochia tomentosa', 'Aristolochia tomentosa', 'Aristolochia tomentosa', 'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii',
                'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii', 'Buddleia davidii',
                'Buddleia x weyeriana', 'Buddleia x weyeriana', 'Buddleia x weyeriana', 'Buddleia x weyeriana', 'Buddleia x weyeriana', 'Buddleia x weyeriana', 'Buddleia x weyeriana', 'Buddleia x weyeriana', 'Buddleia x weyeriana',
                'Buddleia x weyeriana', 'Buddleia x weyeriana', 'Buddleia x weyeriana', 'Chamaecyparis obtusa', 'Chamaecyparis obtusa', 'Chamaecyparis obtusa', 'Chamaecyparis obtusa', 'Chamaecyparis obtusa', 'Chamaecyparis obtusa',
                'Chamaecyparis obtusa', 'Chamaecyparis obtusa', 'Chamaecyparis obtusa', 'Chamaecyparis obtusa', 'Chamaecyparis obtusa', 'Chamaecyparis obtusa', 'Chamaecyparis obtusa', 'Chamaecyfoccia gracilis',
                'Chamaecyfoccia gracilis', 'Chamaecyfoccia gracilis', 'Chamaecyfoccia gracilis', 'Chamaecyfoccia gracilis', 'Chamaecyfoccia gracilis', 'Chamaecyfoccia gracilis', 'Chamaecyfoccia gracilis', 'Chamaecyparis pisifera',
                'Chamaecyparis pisifera', 'Chamaecyparis pisifera', 'Chamaecyparis pisifera', 'Chamaecyparis pisifera', 'Chamaecyparis pisifera', 'Chamaecyparis pisifera', 'Chamaecyparis pisifera', 'Chamaecyparis pisifera',
                'Chamaecyparis pisifera', 'Chamaecyparis pisifera', 'Chamaecyparis pisifera', 'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus alba',
                'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus alba', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia',
                'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia',
                'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia', 'Cornus albernifolia',
                'Cornus canadensis', 'Cornus canadensis', 'Cornus canadensis', 'Cornus canadensis', 'Cornus canadensis', 'Cornus canadensis', 'Cornus canadensis', 'Cornus canadensis', 'Cornus canadensis', 'Cornus canadensis',
                'Cornus canadensis', 'Cornus canadensis', 'Cornus canadensis', 'Euonymus alata', 'Euonymus alata', 'Euonymus alata', 'Euonymus alata', 'Euonymus alata', 'Euonymus alata', 'Euonymus alata', 'Euonymus alata',
                'Euonymus alata', 'Euonymus alata', 'Euonymus alata', 'Euonymus alata', 'Euonymus alata', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima',
                'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima',
                'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Euphorbia pulcherrima', 'Galanthus nivalis', 'Galanthus nivalis', 'Galanthus nivalis', 'Galanthus nivalis', 'Galanthus nivalis',
                'Galanthus nivalis', 'Galanthus nivalis', 'Galanthus nivalis', 'Galanthus nivalis', 'Galanthus nivalis', 'Galanthus nivalis', 'Galanthus nivalis', 'Galanthus nivalis', 'Galanthus nivalisodoratum',
                'Galanthus nivalisodoratum', 'Galanthus nivalisodoratum', 'Galanthus nivalisodoratum', 'Galanthus nivalisodoratum', 'Galanthus nivalisodoratum', 'Galanthus nivalisodoratum', 'Galanthus nivalisodoratum',
                'Galanthus nivalisodoratum', 'Galanthus nivalisodoratum', 'Galanthus nivalisodoratum', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra',
                'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra',
                'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa macra', 'Hakonechloa aureola-macra', 'Hakonechloa aureola-macra', 'Hakonechloa aureola-macra', 'Hakonechloa aureola-macra', 'Hakonechloa aureola-macra',
                'Hakonechloa aureola-macra', 'Hakonechloa aureola-macra', 'Hakonechloa aureola-macra', 'Hakonechloa aureola-macra', 'Hakonechloa aureola-macra', 'Hakonechloa aureola-macra', 'Ilex crenata Hetzii',
                'Ilex crenata Hetzii', 'Ilex crenata Hetzii', 'Ilex crenata Hetzii', 'Ilex crenata Hetzii', 'Ilex crenata Hetzii', 'Ilex crenata Hetzii', 'Ilex crenata Hetzii', 'Ilex crenata Hetzii', 'Ilex crenata Hetzii',
                'Ilex crenata Hetzii', 'Ilex crenata Hetzii', 'Iberis sempervirens', 'Iberis sempervirens', 'Iberis sempervirens', 'Iberis sempervirens', 'Iberis sempervirens', 'Iberis sempervirens', 'Iberis sempervirens',
                'Iberis sempervirens', 'Iberis sempervirens', 'Lamium maculatum', 'Lamium maculatum', 'Lamium maculatum', 'Lamium maculatum', 'Lamium maculatum', 'Lamium maculatum', 'Lamium maculatum', 'Lamium maculatum',
                'Lamium maculatum', 'Lamium maculatum', 'Lamium maculatum', 'Lamium maculatum', 'Mertensia virginica', 'Mertensia virginica', 'Mertensia virginica', 'Mertensia virginica', 'Mertensia virginica', 'Mertensia virginica',
                'Mertensia virginica', 'Mertensia virginica', 'Aristolochata pseudophilus', 'Aristolochata pseudophilus', 'Aristolochata pseudophilus', 'Aristolochata pseudophilus', 'Aristolochata pseudophilus',
                'Aristolochata pseudophilus', 'Aristolochata pseudophilus', 'Aristolochata pseudophilus', 'Aristolochata pseudophilus', 'Aristolochata pseudophilus', 'Chamaecyparis duplicatus', 'Chamaecyparis duplicatus',
                'Chamaecyparis duplicatus', 'Chamaecyparis duplicatus', 'Chamaecyparis duplicatus', 'Chamaecyparis duplicatus', 'Chamaecyparis duplicatus', 'Chamaecyparis duplicatus', 'Chamaecyparis crenata Hetzii',
                'Chamaecyparis crenata Hetzii', 'Chamaecyparis crenata Hetzii', 'Chamaecyparis crenata Hetzii', 'Chamaecyparis crenata Hetzii', 'Chamaecyparis crenata Hetzii', 'Chamaecyparis crenata Hetzii', 'Chamaecyparis',
                'Chamaecyparis', 'Chamaecyparis', 'Chamaecyparis', 'Veronicastrum virginicum', 'Veronicastrum virginicum', 'Veronicastrum virginicum', 'Veronicastrum virginicum', 'Veronicastrum virginicum', 'Veronicastrum virginicum',
                'Veronicastrum virginicum', 'Veronicastrum virginicum', 'Veronicastrum virginicum', 'Veronicastrum virginicum', 'Veronicastrum virginicum', 'Veronicastrum virginicum', 'Veronicastrum virginicum',
                'Veronicastrum vulgaris', 'Veronicastrum vulgaris', 'Veronicastrum vulgaris', 'Veronicastrum vulgaris', 'Veronicastrum vulgaris', 'Veronicastrum vulgaris', 'Veronicastrum vulgaris', 'Veronicastrum vulgaris',
                'Veronicastrum vulgaris', 'Veronicastrum pulchra', 'Veronicastrum pulchra', 'Veronicastrum pulchra', 'Veronicastrum pulchra', 'Veronicastrum pulchra', 'Veronicastrum pulchra', 'Veronicastrum pulchra',
                'Veronicastrum pulchra', 'Veronicastrum pulchra', 'Veronicastrum pulchra']



#Get clusters with MeanShift
X= np.array(df.loc[:,["POINT_X","POINT_Y"]].values.tolist()) # Only using numeric values for now
bandwidth = estimate_bandwidth(X, quantile=0.0595, n_samples=15000)
ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
ms.fit(X)
labels = ms.labels_
cluster_centers = ms.cluster_centers_
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
print("Estimated number of clusters: %d" % n_clusters_)

#Make plot
plt.figure(1)
plt.clf()
colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')

for k, col in zip(range(n_clusters_), colors):
    my_members = labels == k
    cluster_center = cluster_centers[k]
    plt.plot(X[my_members, 0], X[my_members, 1], col + '.')
    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
             markeredgecolor='k', markersize=14)
plt.title('Clusters found by X/Y proximity (before using categorical values): %d' % n_clusters_)
plt.show(); plt.show()