The neural network approach to generate efficient classification rules. Convolution neural network algorithm is a multilayer perceptron that is the special design for identification of two-dimensional data information. Always have more layers: input layer, convolution layer, sample layer and output layer. Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural network. To perform classification task of heart disease dataset, the neural network is trained using convolutions algorithm. The experiment is conducted with heart disease dataset by considering the single and multilayer neural network modes. The proposed algorithm gives detailed analysis of the process of CNN algorithm both the forward process and back propagation. Then we applied improved convolutional neural network to implement the typical heartdata recognition using weka tool. The experimental result show the best classification accuracy compare with existing classification algorithm.
The improvement of an information processing and Memory capacity, the vast amount of data is collected for various data analyses purposes. Data mining techniques are used to get knowledgeable information. The process of extraction of data by using data mining techniques the data get discovered publically and this leads to breaches of specific privacy data. Privacypreserving data mining is used to provide to protection of sensitive information from unwanted or unsanctioned disclosure. In this paper, we analysis the problem of discovering similarity checks for functional dependencies from a given dataset such that application of algorithm (l, d) inference with generalization can anonymised the micro data without loss in utility. [8] This work has presented Functional dependency based perturbation approach which hides sensitive information from the user, by applying (l, d) inference model on the dependency attributes based on Information Gain. This approach works on both categorical and numerical attributes. The perturbed data set does not affects the original dataset it maintains the same or very comparable patterns as the original data set. Hence the utility of the application is always high, when compared to other data mining techniques. The accuracy of the original and perturbed datasets is compared and analysed using tools, data mining classification algorithm.
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