Data mining is an iterative development within which evolution is defined by discovery, through either usual or manual methods. In this paper using the data mining concept to CDMCA classifies two types supervised and unsupervised classifications. Here illustrate the classification of supervised data mining algorithms base on diabetes disease dataset. It encompass the diseases plasma glucose at least mentioned value. The research describes algorithmic discussion of C4.5, SVM, K-NN, PNN, BLR, MLR, CRT, CS-CRT, PLS-DA and PLS-LDA. Here used to compare the performance of computing time, precision value and the data evaluated using 10 fold Cross Validation error rate, the error rate focuses True Positive, True Negative, False Positive and False Negative and Accuracy. The outcome CS-CRT algorithm best. The Best results are achieved by using Tanagra tool. Tanagra is data mining matching set. The accuracy is calculate based on addition of true positive and true negative followed by the division of all possibilities.

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The effect of the parameter values in GA is undeniable. Different working operators as well as different values for the parameters would result in different efficiency and performance for a given problem. The effect of parameters on exploration and exploitation is the main effective factor in GAs. Keeping a balance between the exploration and exploitation during the run would lead to good results. Different parameter has different role in keeping this balance. An adaptive parameter control is proposed. A weight is given to each parameter depend on its power in exploration. The parameters then will be ordered based on their explorative power. A comparison has been made among different orders of the parameters. The results suggest the most suitable parameters in exploration and exploitation in the process of evolution. The results have shown the selection pressure as the most explorative parameter and then mutation and crossover rates has the most effect on exploration.