Question

我尝试使用Kohonen包进行SOM地图分析。我使用了本教程：https://www.shanelynn.ie/self-organising-maps-for-customer-segmentation-using-r/。这是我的代码：

 require(kohonen)
    data = matrix( 
        c(6, 6, 80, 280, 404, 0, 158, 158197, 158197233,
          6, 13, 80, 280, 404, 0, 158, 158197, 158197233,
          6, 13, 80, 283, 404, 0, 158, 158197, 158197233,
          6, 35, 80, 321, 301, 0, 158, 158197, 158197233,
          6, 35, 80, 3131, 200, 0, 158, 158197, 158197233,
          6, 35, 80, 20073, 200, 0, 158, 158197, 158197233,
          6, 35, 80, 183, 200, 0, 158, 158197, 158197233,
          6, 35, 80, 25, 302, 0, 158, 158197, 158197233,
          6, 35, 80, 13744, 200, 0, 158, 158197, 158197233,
          6, 35, 80, 280, 404, 0, 158, 158197, 158197233,
          9, 33, 80, 2859, 200, 0, 158, 158197, 15819736,
          9, 33, 80, 463, 200, 0, 158, 158197, 15819736,
          9, 33, 80, 2065, 200, 0, 158, 158197, 15819736,
          9, 33, 80, 1298, 200, 0, 158, 158197, 15819736,
          9, 33, 80, 86659, 200, 0, 158, 158197, 15819736,
          9, 33, 80, 942, 200, 0, 158, 158197, 15819736,
          9, 33, 80, 2027, 200, 0, 158, 158197, 15819736,
          9, 33, 80, 2979, 200, 0, 158, 158197, 15819736,
          9, 33, 80, 127390, 200, 0, 158, 158197, 15819736,
          9, 33, 80, 1150, 200, 0, 158, 158197, 15819736,
          9, 33, 80, 1150, 200, 0, 158, 158197, 15819736,
          9, 33, 80, 942, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 287, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 90, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 2, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 3314, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 483, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 2, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 543, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 439, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 689, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 797, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 732, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 5403, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 496, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 743, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 3049, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 3064, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 3042, 200, 0, 158, 158197, 15819736,
          9, 40, 80, 3077, 200, 0, 158, 158197, 15819736,
          12, 8, 80,305 ,301 , 0, 142, 1424, 14245,
          12, 8, 80, 10531,200 , 0, 142, 1424, 14245,
          12, 8, 80, 445,400 , 0, 142, 1424, 14245,
          12, 8, 80, 445,400 , 0, 142, 1424, 14245,
          12, 8, 80, 445,400 , 0, 142, 1424, 14245,
          12, 8, 80, 324,400 , 0, 142, 1424, 14245,
          12, 8, 80, 445,400 , 0, 142, 1424, 14245,
          0, 0, 80,0 ,0 , 0, 42, 424, 4245,
          12, 25, 80, 171,200 , 0, 42, 424, 4245,
          12, 25, 80, 2970,200 , 0,42, 424, 4245,
          12, 25, 80, 171,200 , 0, 42, 424, 4245,
          12, 25, 80, 2970,200 , 0, 42, 424, 4245,
          12, 25, 80, 171,200 , 0, 42, 424, 4245,
          12, 25, 80, 2970,200 , 0, 42, 424, 4245,
          15, 32, 80, 2860,200 , 0, 88, 88212, 8821237,
          15, 32, 80, 2859,200 , 0, 88, 88212, 8821237,
          15, 32, 80, 86659,200 , 0, 88, 88212, 8821237,
          15, 32, 80, 22495,200 , 0, 88, 88212, 8821237,
          15, 32, 80, 949,200 , 0, 88, 88212, 8821237,
          15, 32, 80, 1298,200 , 0, 88, 88212, 8821237,
          15, 32, 80, 2027,200 , 0, 88, 88212, 8821237,
          15, 32, 80, 2979,200 , 0, 88, 88212, 8821237,
          15, 32, 80, 103646,200 , 0, 88, 88212, 8821237,
          15, 32, 80, 406,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 1169,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 212,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 2250,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 8496,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 0,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 946,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 716263,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 63,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 63,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 1573,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 63,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 974,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 63,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 926,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 1150,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 800,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 225,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 79,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 408,200 , 0, 88, 88212, 8821237,
          9, 34, 80, 535,200 , 0, 88, 88212, 8821237,
          3, 21, 80, 208,404 , 0, 207, 20746, 2074613,
          3, 49, 80, 1298,200 , 0, 207, 20746, 2074613,
          5, 17, 80, 302,230 , 0, 207, 20746, 2074613,
          8, 28, 80, 2857,200 , 0, 5, 5188, 518810,
          13, 08, 80, 2860,200 , 0, 5, 5188, 518810,
          14, 08, 80, 2860,200 , 0, 5, 5188, 518810,
          15, 58, 80, 208,404 , 0, 66, 66249, 6624966,
          15, 58, 80, 463,200 , 0, 66, 66249, 6624966,
          15, 58, 80, 2065,200 , 0, 66, 66249, 6624966,
          15, 58, 80, 2065,200 , 0, 66, 66249, 6624966,
          13, 05, 80, 608,200 , 0, 88, 88212, 8821240,
          13, 08, 80, 608,200 , 0, 88, 88212, 8821240,
          13, 11, 80, 608,200 , 0, 88, 88212, 8821240,
          13, 14, 80, 608,200 , 0, 88, 88212, 8821240,
          13, 17, 80, 608,200 , 0, 88, 88212, 8821240,
          13, 20, 80, 608,200 , 0, 88, 88212, 8821240 ), 

         nrow=100,             
         ncol=9,              
         byrow = TRUE)        

    data_train <- data[, c(1,2,4,5,7,8,9)]
    #data_train <- data[, c(2,4,5,8)]
    data_train_matrix <- as.matrix(scale(data_train))
    som_grid <- somgrid(xdim = 3, ydim=4, topo="hexagonal")



    som_model <- som(data_train_matrix, 
      grid=som_grid, 
    rlen=500, 
     alpha=c(0.05,0.01), 
     keep.data = TRUE )


    #training proces
    plot(som_model, type="changes")
    #nodes 
    plot(som_model, type="count", main="Node Counts")
    #distance
    #plot(som_model, type="dist.neighbours", main = "SOM neighbour distances")
    #codes and weight vectors
    #plot(som_model, type="codes")
    #heatmap
    plot(som_model, type = "property", property = getCodes(som_model)[,4], main="Heat map - parameter 4")

这是我的地图可视化：

地图没问题。我的问题是：在某种程度上如何找到白色节点中的数据？我看一下getCodes（som_model），但只有地图编号。所以我知道白色节点是V4。但是节点V4中的数据是什么？我查看了所有som_model值，但它没有帮助。有任何想法吗？

> getCodes(som_model)[,4]
          V1           V2           V3           V4           V5           V6           V7           V8 
-0.727734454 -0.183272487 -0.342681905  2.361366190 -0.343764866 -0.343764866  1.298987948 -0.343532184 
          V9          V10          V11          V12 
-0.343764307  1.350552793 -0.003492471 -0.343764866

我希望结果像这样：

    > inV4
[1]  2 25  0

可以在Kohonen套餐中这样做吗？

更新：在Kohonen包中看起来不可能轻松实现。所以我试试这个：

# find which node is white
q <- getCodes(som_model)[,4]
for (i in 1:length(q)){
  if(q[i]>2){
    t<- q[i]

  }
}
# find name od node 
node <- names(t)
#remove "V" letter from node name
mynode <- gsub("V","",node)

#find which node has which input ???
mydata2 <- som_model$unit.classif
print (myadat2)

#choose just imputs which go to right node
result <- vector('list',length(mydata2))
for (i in 1:length(mydata2)){
  result <- som_model$unit.classif== mynode
}

#remove FALSE results
result2 <- which(result == TRUE)

#write all input line 
for (i in 1:length(result2)){
  print (data[result2[i],])
}

但我不确定这是否正确。我不确定这是否给了我正确的结果输入。有什么方法可以检查吗？

Answer 1

很抱歉，现在来不及帮助您。它可能会帮助别人。

由于您未设置随机种子，因此我们无法完全重现您的结果。当我运行您的代码时，我得到了情节

我得到了代码：

getCodes(som_model)[,4]
        V1         V2         V3         V4         V5         V6         V7 
-0.3437649 -0.3437649  2.3146262  0.4037323 -0.3437649 -0.6034393 -0.3434484 
        V8         V9        V10        V11        V12 
 0.6730257  0.2089917 -0.3437649 -0.1038754  2.6302823

对我来说，白色节点是V12。通过查看属于som对象的单位分类，可以获得V12中的数据点。

som_model$unit.classif
  [1] 12 12 12  9  9  9  9  9  9 12 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
 [26] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10  3  2  3  3  3  3  3  6  6  6
 [51]  6  6  6  6  1  1  1  1  1  1  1  1  1  1  5  5  5  5  5  5  7  5  5  5  5
 [76]  5  5  5  5  5  5  5  5  5  8  8  8  6  6  6  4  4  4  4  2  2  2  2  2  2

要获取V12中的要点，只需使用：

which(som_model$unit.classif == 12)
[1]  1  2  3 10

SOM地图 - kohonen包 - 数值结果

1 个答案: