我已经根据病房算法对一些种群进行了聚类，现在我想对它们进行聚类并配对每个聚类内的种群
我已经找到了写字典的方法，该字典包含所有股票以及它们与之相关的树状图的颜色，但是没有办法让所有在一定距离内聚集在一起的股票。
clusterdict = defaultdict(list)
for ind,clust in zip(den['ivl'],den['leaves']):
clusterdict[clust].append(ind)
这是它返回的字典
defaultdict(<type 'list'>, {0: [u'GOLD'], 1: [u'AEM'], 2: [u'CDE'], 3:
[u'CLF'], 4: [u'FOE'], 5: [u'HL'], 6: [u'LPX'], 7: [u'MAS'], 8: [u'NEM'],
9: [u'NUE'], 10: [u'OLN'], 11: [u'PPG'], 12: [u'MUX'], 13: [u'WY'], 14:
[u'X'], 15: [u'KGC'], 16: [u'AKS'], 17: [u'ALB'], 18: [u'PAAS'], 19:
[u'FCX'], 20: [u'CCJ'], 21: [u'CENX'], 22: [u'SSRM'], 23: [u'STLD'], 24:
[u'TREX'], 25: [u'IAG'], 26: [u'EGO'], 27: [u'TRQ'], 28: [u'AUY'], 29:
[u'NG'], 30: [u'SA'], 31: [u'HUN'], 32: [u'NGD'], 33: [u'WPM'], 34:
[u'CF'], 35: [u'TECK'], 36: [u'LYB'], 37: [u'TROX'], 38: [u'AG'], 39:
[u'MOS'], 40: [u'FSM'], 41: [u'PVG'], 42: [u'SLCA'], 43: [u'SAND'], 44:
[u'AGI'], 45: [u'CSTM'], 46: [u'BTG'], 47: [u'ESI'], 48: [u'AXTA'], 49:
[u'SUM'], 50: [u'UNVR'], 51: [u'CC'], 52: [u'AA'], 53: [u'KL'], 54:
[u'DWDP'], 55: [u'NTR']})
如果有帮助的话，这是树状图中的链接数组
[[ 5. 27. 1.19107273 2. ]
[ 12. 28. 1.86356669 2. ]
[ 15. 29. 2.10022495 2. ]
[ 32. 56. 2.85571413 3. ]
[ 25. 40. 3.2928348 2. ]
[ 43. 46. 3.62678069 2. ]
[ 38. 44. 3.66910652 2. ]
[ 2. 18. 3.99048391 2. ]
[ 57. 58. 4.43112104 4. ]
[ 59. 62. 4.54448187 5. ]
[ 16. 26. 4.96083261 2. ]
[ 41. 61. 6.63829892 3. ]
[ 19. 35. 8.17068596 2. ]
[ 60. 66. 8.21948828 4. ]
[ 20. 67. 8.75546161 4. ]
[ 4. 6. 9.37382844 2. ]
[ 22. 30. 10.72164076 2. ]
[ 50. 54. 11.62929046 2. ]
[ 21. 37. 12.44096076 2. ]
[ 13. 31. 12.76859026 2. ]
[ 0. 70. 12.98710004 5. ]
[ 47. 64. 13.63169703 5. ]
[ 9. 71. 14.56215301 3. ]
[ 45. 63. 15.24100602 3. ]
[ 65. 69. 16.07353304 9. ]
[ 48. 78. 17.15135825 4. ]
[ 14. 42. 18.04499759 2. ]
[ 10. 74. 18.29581966 3. ]
[ 7. 81. 20.16860024 5. ]
[ 1. 33. 20.58004761 2. ]
[ 34. 55. 21.00435166 2. ]
[ 3. 76. 22.71769878 6. ]
[ 68. 79. 24.00631196 5. ]
[ 77. 80. 25.43375614 14. ]
[ 49. 82. 26.54461207 3. ]
[ 23. 75. 29.11645193 3. ]
[ 72. 87. 30.22339441 8. ]
[ 8. 85. 30.49582653 3. ]
[ 51. 90. 30.60054445 4. ]
[ 73. 83. 33.15254175 5. ]
[ 11. 86. 38.35470296 3. ]
[ 39. 92. 42.06555848 9. ]
[ 84. 88. 51.03959914 10. ]
[ 36. 52. 51.76022424 2. ]
[ 91. 95. 60.06346861 8. ]
[ 89. 93. 67.07753611 17. ]
[ 17. 100. 83.22804338 9. ]
[ 96. 97. 83.37433519 12. ]
[ 101. 103. 104.12479363 29. ]
[ 94. 102. 123.76112823 13. ]
[ 24. 53. 130.46110771 2. ]
[ 104. 106. 149.73602378 31. ]
[ 98. 107. 183.96335166 41. ]
[ 99. 105. 195.52651673 15. ]
[ 108. 109. 520.2572738 56. ]]