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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.

Chart recognition system from PDF files is a relatively young research field where techniques and algorithms are proposed to identify type of charts and interpret them. This paper focus on recognition of chart type that is a part of PDF document using texture features and classification algorithm. Eleven types of texture features and three classifiers, namely, Multilayer perceptron, support vector machine and K nearest neighbour, are used. Performance analysis of the proposed chart type recognition systems show that texture features for chart type recognition has promising future and produces best result while using KNN and SVM algorithm.

Energy Efficient in the VoIP transmission is the major criteria and issues to be maintained the packet and data while processing VoIP (QoS)