Body: <p>I have trained (with different sizes, learning rates, and epochs) a SOM network to cluster <a href="https://archive.ics.uci.edu/ml/datasets/iris" rel="nofollow noreferrer">the Iris dataset</a>. The instances associated with the class <code>setosa</code> have been mainly fitted to a 1-2 BMUs. In the case of <code>virginica</code>, the instances have also be associated with only a few BMUs. However, in the case of <code>versicolor</code> instances, many BMUs have been associated with them.</p>
  <p>Is this normal?</p>
  <pre><code>Setosa
  0. 1846
  1. 1846
  2. 1846
  3. 1846
  4. 1846
  5. 1846
  6. 1846
  7. 1846
  8. 1846
  9. 1846
  10. 1846
  11. 1846
  12. 1846
  13. 1846
  14. 1846
  15. 1846
  16. 1846
  17. 1846
  18. 1846
  19. 1846
  20. 1846
  21. 1846
  22. 1846
  23. 1846
  24. 1846
  25. 1846
  26. 1846
  27. 1846
  28. 1846
  29. 1846
  30. 1846
  31. 1846
  32. 1846
  33. 1846
  34. 1846
  35. 1846
  36. 1846
  37. 1846
  38. 1846
  39. 1846
  40. 1846
  41. 1620
  42. 1846
  43. 1846
  44. 1846
  45. 1846
  46. 1846
  47. 1846
  48. 1846
  49. 1846
  
  Versicolor
  50. 652
  51. 652
  52. 652
  53. 1259
  54. 696
  55. 1394
  56. 652
  57. 490
  58. 696
  59. 490
  60. 490
  61. 1059
  62. 1304
  63. 696
  64. 490
  65. 652
  66. 1400
  67. 490
  68. 696
  69. 490
  70. 652
  71. 1574
  72. 696
  73. 832
  74. 696
  75. 696
  76. 696
  77. 652
  78. 696
  79. 490
  80. 490
  81. 490
  82. 444
  83. 696
  84. 1129
  85. 1084
  86. 652
  87. 696
  88. 25
  89. 584
  90. 490
  91. 789
  92. 1034
  93. 490
  94. 854
  95. 29
  96. 584
  97. 877
  98. 490
  99. 809
  
  Virginica
  100. 652
  101. 696
  102. 652
  103. 652
  104. 652
  105. 652
  106. 877
  107. 652
  108. 696
  109. 652
  110. 652
  111. 696
  112. 652
  113. 696
  114. 652
  115. 652
  116. 652
  117. 652
  118. 652
  119. 696
  120. 652
  121. 652
  122. 652
  123. 696
  124. 652
  125. 652
  126. 696
  127. 652
  128. 652
  129. 652
  130. 652
  131. 652
  132. 652
  133. 696
  134. 696
  135. 652
  136. 652
  137. 652
  138. 652
  139. 652
  140. 652
  141. 652
  142. 696
  143. 652
  144. 652
  145. 652
  146. 696
  147. 652
  148. 652
  149. 652
  </code></pre>
  <p>Now, I have a diagram. It doesn't look bad.</p>
  <p><a href="https://i.sstatic.net/7eqOz.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/7eqOz.png" alt="enter image description here" /></a></p>
  
Tags: <neural-networks><unsupervised-learning><clustering><self-organizing-map><iris-dataset>
Title: Is it normal that SOM clusters the instances with the "versicolor" class into multiple different BMUs?
UserDisplayName: Ameba kupiec
ThreadText: # Is it normal that SOM clusters the instances with the "versicolor" class into multiple different BMUs?
  
  (Asked by: username_0 on 2018-06-14)
  
  I have trained (with different sizes, learning rates, and epochs) a SOM network to cluster the Iris dataset. The instances associated with the class setosa have been mainly fitted to a 1-2 BMUs. In the case of virginica, the instances have also be associated with only a few BMUs. However, in the case of versicolor instances, many BMUs have been associated with them.
  Is this normal?
  Setosa
  0. 1846
  1. 1846
  2. 1846
  3. 1846
  4. 1846
  5. 1846
  6. 1846
  7. 1846
  8. 1846
  9. 1846
  10. 1846
  11. 1846
  12. 1846
  13. 1846
  14. 1846
  15. 1846
  16. 1846
  17. 1846
  18. 1846
  19. 1846
  20. 1846
  21. 1846
  22. 1846
  23. 1846
  24. 1846
  25. 1846
  26. 1846
  27. 1846
  28. 1846
  29. 1846
  30. 1846
  31. 1846
  32. 1846
  33. 1846
  34. 1846
  35. 1846
  36. 1846
  37. 1846
  38. 1846
  39. 1846
  40. 1846
  41. 1620
  42. 1846
  43. 1846
  44. 1846
  45. 1846
  46. 1846
  47. 1846
  48. 1846
  49. 1846
  
  Versicolor
  50. 652
  51. 652
  52. 652
  53. 1259
  54. 696
  55. 1394
  56. 652
  57. 490
  58. 696
  59. 490
  60. 490
  61. 1059
  62. 1304
  63. 696
  64. 490
  65. 652
  66. 1400
  67. 490
  68. 696
  69. 490
  70. 652
  71. 1574
  72. 696
  73. 832
  74. 696
  75. 696
  76. 696
  77. 652
  78. 696
  79. 490
  80. 490
  81. 490
  82. 444
  83. 696
  84. 1129
  85. 1084
  86. 652
  87. 696
  88. 25
  89. 584
  90. 490
  91. 789
  92. 1034
  93. 490
  94. 854
  95. 29
  96. 584
  97. 877
  98. 490
  99. 809
  
  Virginica
  100. 652
  101. 696
  102. 652
  103. 652
  104. 652
  105. 652
  106. 877
  107. 652
  108. 696
  109. 652
  110. 652
  111. 696
  112. 652
  113. 696
  114. 652
  115. 652
  116. 652
  117. 652
  118. 652
  119. 696
  120. 652
  121. 652
  122. 652
  123. 696
  124. 652
  125. 652
  126. 696
  127. 652
  128. 652
  129. 652
  130. 652
  131. 652
  132. 652
  133. 696
  134. 696
  135. 652
  136. 652
  137. 652
  138. 652
  139. 652
  140. 652
  141. 652
  142. 696
  143. 652
  144. 652
  145. 652
  146. 696
  147. 652
  148. 652
  149. 652
  
  Now, I have a diagram. It doesn't look bad.
  
  **Tags:** neural-networks, unsupervised-learning, clustering, self-organizing-map, iris-dataset
  
  ---
  
  ## 0 answers
  
  