大佬教程收集整理的这篇文章主要介绍了尝试使用纯 Python 获得 k-Medoids 聚类，基于其距离矩阵仅使用来自 python 的本机库，大佬教程大佬觉得挺不错的，现在分享给大家，也给大家做个参考。
我在这里挣扎了很长时间试图完成这件事......我想要做的是用纯 Python 实现 k-MedoID PAM 算法......我的意思是没有其他库而不是原生库蟒蛇...
我有下表和距离矩阵... 那边有人可以帮我解决这个问题吗？也许你们中的一些人可以帮助我只是试图了解我如何完成这项工作...... 我编写了以下代码，但无法正常工作
def buildKMedoIDs(distances,matrixfiles):
def tryclusters(distances,Lista):
print(distances)
conjuntoDiferencas = distances.copy()
newEnsemble = []
for n in distances:
for m in distances:
for i,j in zip(n[1],m[1]):
if i < j:
newElement = conjuntoDiferencas.pop(conjuntoDiferencas.index(n))
newEnsemble.append(newElement)
print(newSet)
我有一些示例数据，用于 K = 2
对于此示例数据，2 个集群是 highway_bost174,ibis_142
| highway_bost174 | ibis_142 | street_par88 | opencountry_241 | waterfall23 | fIEld26 | @H_74_28@mountain_030horse_081 | bison_052 | ibis_040 | 总距离 | @H_262_27@highway_bost174 | 0 | 8.708170812 | 4.088197921 | 11.366319880 | 12.63876329 | 11.07823394 | 10.02510284 | 8.415467337 | 8.194840093 | 13.45505618 | 87.9701522869999 | @H_262_27@ibis_142 | 8.708170812 | 0 | 10.518235207 | 7.668395996 | 10.5223999 | 7.302185059 | 6.417022705 | 6.146172005 | 10.44835499 | 5.149291993 | 72.8802286650000 | @H_262_27@street_par88 | 4.088197921 | 10.51823521 | 0. | 11.135904053 | 11.47283127 | 10.69156812 | 9.663827636 | 10.65966088 | 9.392413014 | 12.5860189 | 90.2086570010000 | @H_262_27@opencountry_241 | 11.36631988 | 7.668395996 | 11.135904053 | 0. | 13.31494141 | 2.754882813 | 3.998626709 | 9.028326501 | 12.14570309 | 8.675354003 | 80.0884544509999 | @H_262_27@waterfall23 | 12.63876329 | 10.5223999 | 11.472831274 | 13.314941407 | 0 | 12.66552734 | 11.40634155 | 12.6048929 | 11.43774673 | 8.79888916 | 104.8623335570000 | @H_262_27@fIEld26 | 11.07823394 | 7.302185059 | 10.691568116 | 2.754882813 | 12.66552734 | 0 | 3.349212646 | 8.966176812 | 11.82766924 | 8.203674316 | 76.8391302850000 | @H_262_27@ @H_824_55@mountain_03010.02510284 | 6.417022705 | 9.663827636 | 3.998626709 | 11.40634155 | 3.349212646 | 0 | 8.78585096 | 11.99428394 | 7.732574462 | 73.3728434489999 | @H_262_27@马_081 | 8.415467337 | 6.146172005 | 10.65966088 | 9.028326501 | 12.6048929 | 8.966176812 | 8.78585096 | 0 | 8.054160894 | 11.09364108 | 83.75434938 | @H_262_27@bison_052 | 8.194840093 | 10.44835499 | 9.392413014 | 12.145703089 | 11.43774673 | 11.82766924 | 11.99428394 | 8.054160894 | 0 | 12.86955948 | 96.3647314629999 | @H_262_27@ibis_040 | 13.45505618 | 5.149291993 | 12.586018896 | 8.675354003 | 8.79888916 | 8.203674316 | 7.732574462 | 11.09364108 | 12.86955948 | 0 | 88.5640595690000 |
|---|
distances= [('highway_bost174',[0.0,8.708170812,4.088197921,11.366319879999999,12.638763287,11.078233943,10.025102839,8.415467337,8.194840093,13.455056175000001]),('ibis_142',[8.708170812,0.0,10.518235207,7.668395996,10.522399903,7.302185059,6.417022705,6.146172005,10.448354985,5.149291993]),('street_par88',[4.088197921,11.135904053,11.472831274,10.691568116,9.663827636,10.659660884000001,9.392413013999999,12.586018896]),('opencountry_241',[11.366319879999999,13.314941407,2.754882813,3.998626709,9.028326501,12.145703089000001,8.675354002999999]),('waterfall23',[12.638763287,12.665527344000001,11.406341552,12.6048929,11.43774673,8.79888916]),('fIEld26',[11.078233943,3.349212646,8.966176812,11.827669236000002,8.203674316]),('mountain_030',[10.025102839,8.78585096,11.994283939999999,7.7325744620000005]),('horse_081',[8.415467337,8.054160893999999,11.093641082000001]),('bison_052',[8.194840093,12.869559482]),('ibis_040',[13.455056175000001,5.149291993,12.586018896,8.675354002999999,8.79888916,8.203674316,7.7325744620000005,11.093641082000001,12.869559482,0.0])]
matrixfiles = [('highway_bost174',0.0])]
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