The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Introduction to fcmfd

Introduction

The fcmfd package implements fuzzy clustering for ordinal Likert-type data using triangular fuzzy numbers (TFNs).

Example Dataset

data(sim_likert7)

head(sim_likert7)
#>   Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12
#> 1  2  2  3  2  2  3  2  1  2   2   2   2
#> 2  2  2  2  3  2  1  2  2  2   2   2   2
#> 3  2  1  2  2  2  3  2  2  2   2   2   2
#> 4  2  2  2  2  2  2  1  3  2   2   2   2
#> 5  2  2  2  2  2  3  2  3  1   2   2   2
#> 6  2  2  2  2  2  2  2  2  2   3   2   1

Running the Clustering Algorithm

result <- fcmTFN(
  data = sim_likert7,
  option = "B",
  k_values = 2:6
)
#> Running k = 2 
#> Running k = 3 
#> Running k = 4 
#> Running k = 5 
#> Running k = 6

summary(result)
#> 
#> Fuzzy C-Means Clustering for TFN
#> ---------------------------------
#> 
#> Optimal number of clusters (k):  3 
#> 
#> Weights:
#> wc = 0.61997 
#> ws = 0.38003 
#> 
#> Iterations: 11 
#> 
#> Scale configuration:
#> Type   : symmetric 
#> Option : B 
#> 
#> Xie-Beni values:
#> k = 2 : 0.067732 
#> k = 3 : 0.044298 
#> k = 4 : 4.477249e+14 
#> k = 5 : 4.077275e+15 
#> k = 6 : 3.137093e+15

Cluster Assignment

clusters <- cluster_assignment(result)

table(clusters)
#> clusters
#>   1   2   3 
#> 100 100 100

Prototype Interpretation

prototype_matrix(result)
#>         Cluster_1 Cluster_2 Cluster_3
#> Var1_l   3.007430  1.080729  4.969121
#> Var1_c   3.061097  1.133164  5.060103
#> Var1_r   2.970013  1.140116  5.059868
#> Var2_l   3.007920  1.080556  5.029623
#> Var2_c   3.061827  1.102351  4.957240
#> Var2_r   3.006383  1.112967  4.945130
#> Var3_l   2.972091  1.110017  5.017653
#> Var3_c   3.027725  1.102007  5.028846
#> Var3_r   2.992749  1.093108  4.988776
#> Var4_l   3.006059  1.071020  4.989099
#> Var4_c   3.005318  1.113128  4.993825
#> Var4_r   2.970551  1.152167  4.984855
#> Var5_l   4.007277  2.021546  5.969110
#> Var5_c   4.060629  2.038220  6.060074
#> Var5_r   3.969568  2.023083  6.059839
#> Var6_l   4.007553  1.952083  6.029599
#> Var6_c   4.061397  1.975648  5.957210
#> Var6_r   4.005926  1.997163  5.945100
#> Var7_l   3.971919  2.040613  6.017642
#> Var7_c   4.027324  1.932746  6.028818
#> Var7_r   3.992573  2.034464  5.988764
#> Var8_l   4.005667  1.995309  5.989075
#> Var8_c   4.005073  2.045521  5.993807
#> Var8_r   3.970209  2.044463  5.984830
#> Var9_l   5.006898  3.021520  6.850721
#> Var9_c   5.060260  3.038197  6.930289
#> Var9_r   4.969161  3.023056  6.911577
#> Var10_l  5.007289  2.952065  6.909242
#> Var10_c  5.061273  2.975639  6.918142
#> Var10_r  5.005549  2.997138  6.858979
#> Var11_l  4.971420  3.040580  6.881060
#> Var11_c  5.027004  2.932725  6.929930
#> Var11_r  4.992176  3.034437  6.880635
#> Var12_l  5.005315  2.995286  6.890936
#> Var12_c  5.004601  3.045489  6.898119
#> Var12_r  4.969811  3.044437  6.887700

Xie-Beni Index Visualization

plot_xb(result)

Conclusion

This vignette demonstrated the basic workflow for fuzzy clustering of Likert-type data using the fcmfd package.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.