A Clustering Approach for the -Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm

Pseudocode 1

The pseudocode of FC-BFO algorithm.

   Input: Dataset   Output: Clusters  

   Parameters:

    chemotaxis step;   reproduction step;     elimination-dispersal step;  

dimension of the search space;     total number of bacteria in the population;

  number of chemotactic steps;   number of reproduction steps;   number of

elimination-dispersal steps;   swim step;   probability of elimination-

dispersal;   step size during tumble.

 FC-BFO Algorithm:

    (1)   Initialize the parameters   where

      and

    (2)   Elimination-dispersal loop :

    (3)   Reproduction loop :

    (4)   Chemotaxis loop :

    (5)   Apply a chemotaxis step for the th bacterium (where, )

    (6)   Calculate fitness function

    (7)   Store the value as   to find a better fitness function

    (8)   Tumble: Generate a random vector   with each element of a numerical

     attribute

    (9)   Generate a random direction of categorical attribute

   (10) Move: Make a move in the direction of the tumble for the bacterium

   (11) Compute fitness function   with  

   (12) Swim:

   (13) Initialize the swim counter

   (14) While     do

   (15)

   (16) if   <     then

   (17)

   (18)

   (19) else

   (20) End if

   (21)  End while

   (22) If ,   then go to step 4.

   (23) Reproduction     of   bacteria with higher OB.

   (24) If ,   then go to step 3. Start again with the Chemotaxis step.

   (25) Elimination-dispersal   : eliminate the bacterium that has highest

     fitness   value and disperse it to a random location

   (26) For   to   :

   (27) Anonymize ()

   (28) End for

   (29) End