有人可以帮助修改下面的工作示例以从共享数据创建集群吗？
该示例使用来自 Scikit-Learn 的 Mean Shift 聚类来识别农艺设施中相似/位于同一地点的植物物种的斑块。
这类问题中除了数值外还使用分类值的类似问题之前也有人问过，但我认为这个例子不同，原因如下：这个问题中的非数值不能简单地用一和零编码虚拟值。例如，我们不能对“Aristolochia macrophylla”和“Aristolochia durior”之类的值进行 One-Hot 编码，因为名称中具有这种相似性的物种除了给定的地理邻近性之外，还需要根据它们的家庭聚集在一起通过 X 和 Y 值。创建集群时，名称的相似性与位置一样重要。
我尝试了两件事：为物种名称中的字母分配任意数值，以表明具有相似拼写的名称在数轴上会更靠近。我打算对值应用自动缩放并使用 X 和 Y 坐标插入脚本。这不起作用，因为不同的名称最终在数字上非常相似。
我合并分类值的另一个尝试是使用 Levenstein 距离。但是距离的输出仅基于比较两个值。如果您输出显示每个字符串与所有其他字符串的距离，您如何将该结果实现为 Meanshift 算法的输入？
无论如何，这里是目前仅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。
谢谢
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,
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-75.933274837,-75.932816577,-75.932958943,-75.932872736,-75.933039998,-75.932930987,-75.932975423,-75.932987859,-75.932944342,-75.932984985,-75.933102016,-75.933042959,-75.935432474,-75.93539475,-75.935456177,
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-75.933882234,-75.934830229,-75.934978045,-75.934357619,-75.934605828,-75.934754661,-75.934743056,-75.934130125,-75.935928887,-75.936286533,-75.936425628,-75.936477105,-75.935622798,-75.935607342,-75.936576534,
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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,
38.951198089,38.951901959,38.94884403,38.948941748,38.949353979,38.949035993,38.949016785,38.94887402,38.948802413,38.948722997,38.94868013,38.948698153,38.948609493,38.948407937,38.948413538,38.94884251,
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()