$D$   $K'$
  ---- ------------------------------------ -------- ----- ------
  1    Abalone                                 4,177     7     28
  2    Breast Cancer Wisconsin (Original)        683     9      2
  3    Breast Tissue                             106     9      6
  4    Ecoli                                     336     7      8
  5    Glass Identification                      214     9      6
  6    Heart Disease                             297    13      5
  7    Ionosphere                                351    34      2
  8    Iris (Bezdek)                             150     4      3
  9    ISOLET                                  7,797   617     26
  10   Landsat Satellite (Statlog)             6,435    36      6
  11   Letter Recognition                     20,000    16     26
  12   MAGIC Gamma Telescope                  19,020    10      2
  13   Multiple Features (Fourier)             2,000    76     10
  14   Musk (Clean2)                           6,598   166      2
  15   Optical Digits                          5,620    64     10
  16   Page Blocks Classification              5,473    10      5
  17   Pima Indians Diabetes                     768     8      2
  18   Shuttle (Statlog)                      58,000     9      7
  19   Spambase                                4,601    57      2
  20   SPECTF Heart                              267    44      2
  21   Wall-Following Robot Navigation         5,456    24      4
  22   Wine Quality                            6,497    11      7
  23   Wine                                      178    13      3
  24   Yeast                                   1,484     8     10

  : \[tab\_data\_set\] Data Set Descriptions ($N$: \# points, $D$: \# attributes, $K'$: \# classes)

The performance of the IMs was quantified using two effectiveness (quality) and two efficiency (speed) criteria:

-   [**Initial SSE**]{}: This is the SSE value calculated after the initialization phase, before the clustering phase. It 