OPTIMIZING THE EFFICIENCY IN DUPLICATE DETECTION USING BN APPROACH

Author (s) : S.Anuradha and Dr.R.Sharmila

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Abstract :
Duplicate detection a nontrivial task is the fact that duplicates are not exactly equal, often due to errors in the data and the objects. Methods devised for duplicate detection in a single relation do not directly apply to XML data, due to the differences between the two data models. In existing system represents the XML data. To do this, we can compare the corresponding leaf node values of both objects. In this work existing system  that the hierarchical organization of XML data helps in detecting duplicate elements, since descendant elements can be detected to be similar, which increases the similarity of the ancestors, and so on in a top-down fashion. Existing system only considering only the XML Data files to detect duplicate and non duplicate files while considering the XML data Object also improves the efficiency of the duplicate detection than the existing system, so our proposed system finds the duplicate detection of XML data and XML Objects with different structure representation of the input files. Derive the conditional probability proposed system apply machine learning method such as SVM. Support vector machines (SVMs) also support vector network share supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.

Keywords : Support vector machines (SVMs), XML data and XML Objects

Conference Date : 30/08/2019

Pages : 22

Paper ID : 19021