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Table 2 Quality comparison of different clustering algorithms on bioinformatics datasets

From: A multiple kernel density clustering algorithm for incomplete datasets in bioinformatics

Dataset Measure metrics PBC-A PBC-R MFCCs DLBCL-B Wine WDBC MPE-A MPE-R ESR
MKDCI F-m 0.351 0.360 0.728 0.749 0.652 0.858 0.470 0.482 0.491
  aRe 0.956 0.953 0.406 0.526 0.704 0.382 0.693 0.689 0.852
  NMI 0.351 0.362 0.692 0.532 0.414 0.495 0.538 0.554 0.446
  AMI 0.070 0.076 0.615 0.496 0.379 0.453 0.429 0.438 0.219
DBSCAN (MinPts=4, ε1) F-m 0.660 0.665 0.509 0.510 0.576 0.811 0.448 0.452 0.350
  aRe 0.999 0.998 0.858 0.956 0.772 0.602 0.796 0.794 0.967
  NMI 0.023 0.026 0.221 0.054 0.361 0.395 0.492 0.499 0.060
  AMI 0.005 0.005 0.124 0.039 0.269 0.295 0.347 0.347 0.003
HDBSCAN (MinPts=4) F-m 0.623 0.627 0.785 0.565 0.620 0.853 0.265 0.271 0.332
  aRe 0.998 0.998 0.260 0.985 0.715 0.386 0.926 0.923 0.989
  NMI 0.029 0.032 0.686 0.174 0.386 0.469 0.518 0.523 0.082
  AMI 0.019 0.020 0.613 0.115 0.353 0.373 0.335 0.337 0.020
DENCLUE2.0 (ε2,h=std(X)/5) F-m 0.023 0.025 0.415 0.493 0.372 0.007 0.304 0.308 0.650
  aRe 0.997 0.996 0.983 0.987 0.908 0.998 0.708 0.699 0.685
  NMI 0.344 0.347 0.105 0.184 0.385 0.322 0.472 0.478 0.472
  AMI 0.061 0.064 0.018 0.114 0.122 0.002 0.392 0.396 0.201
PFClust F-m 0.315 0.320 0.375 0.442 0.373 0.432 0.202 0.207 0.271
  aRe 0.981 0.978 0.887 0.993 0.971 0.988 0.998 0.998 0.872
  NMI 0.002 0.002 0.123 0.043 0.033 0.019 0.024 0.028 0.135
  AMI 0.001 0.001 0.094 0.001 0.001 0.007 0.006 0.007 0.111
Parameters ε 1 24.657 24.657 0.306 19.819 3.626 20.413 2.221 2.221 1.426
  ε 2 19.591 19.591 0.306 0.413 6.552 1.426 0.432 0.432 1.853
  1. MinPts is the minimum number of data samples required to form a cluster, ε1 is the maximum distance between two data samples for them to be considered as in the same neighborhood, ε2 is the convergence threshold for density attractors and h is the parameter of a Gaussian kernel. ε1 and ε2 are the corresponding parameters when the better clustering results are obtained for F-m evaluation metric during clustering with ten random values of the parameters between 0.0 and 50.0