2.1. Fuzzy Discretization
In order to avoid the crisp discretization used by cAnt-Miner (Otero, Freitas, & Johnson, 2008), our method extends the crisp partition by discretizing the values of an attribute into fuzzy partitions. First a threshold x is determined in the same way as cAnt-Miner (the one that leads to the minimum entropy (Quinlan, 1993)). We assume that the threshold is a fuzzy number around x. Then the values of continuous attribute are transformed into membership degrees of the fuzzy values Ai(low)<< x, Ai(average)≈ x and Ai(high)>> x. Where the membership functions of the continuous attribute 
to a fuzzy value Ai<< x is calculated by Figure 1:
=
(1)Figure 1. Fuzzy discretization of a continuous attribute
The membership functions of the continuous attribute Ai to a fuzzy value Ai≈ x is calculated by:
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(2)The membership functions of the continuous attribute Ai to a fuzzy value Ai>> x is calculated by:
=
(3)Where parameters (a, b, c et d) determine the boundaries of the fuzzy area and so the degree of fuzziness. The user sets these parameters for the best accuracy. Note that other ways of generating the fuzzy partitions Liu, Hussain, Tan, & Dash, 2002, Marsala, 1998 can be used in our method.