Please help me for Class-Attribute Interdependence Maximization algorithm(CAIM)

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artificial-intelligence

Hi currently I am working on CAIM algorithm , and facing problem with the interpreation of the algorithm.
Algorithm is as below:
Given: Data consisting of M examples, S classes, and continuous attributes Fi.
For every Fi do:

Step1.
1.1 find maximum (dn) and minimum (do) Values of Fi
1.2 form a set of all distinct values of Fi in ascending order, and initialize all possible interval boundaries B with minimum, maximum and all the midpoints of all the adjacent pairs in the set
1.3 set the initial discretization scheme as
D:{[d0 ,dn ]} , set GlobalCAIM=0

Step2.
2.1 initialize k=1;
2.2 tentatively add an inner boundary, which
is not already in D, from B, and calculate corresponding CAIM value
2.3 after all the tentative addition have been tried accept the one with the highest value of CAIM
2.4 if (CAIM > GlobalCAIM or k

2.5 set k=k+1 and go to 2.2
Output: Discretization scheme D
Here I dont know how to generate B and what does exactly meant by k? Because it is checking the condition k
Please suggest me if any one have proper real time example or anything like that.