Incremental Approach of Neural Network in Back Propagation Algorithms for Web Data Mining

A. P. Tawdar, M. S. Bewoor, S. H. Patil

Abstract


Text Classification is also called as Text Categorization (TC), is the task of classifying a set of text documents automatically into different categories from a predefined set. If a text document relates to exactly one of the categories, then it is called as single-label classification task; otherwise, it is called as multi-label classification task. For Information Retrieval (IR) and Machine Learning (ML), TC uses several tools and has received much attention in the last decades. In this paper, first classifies the text documents using MLP based machine learning approach (BPP) and then return the most relevant documents. And also describes a proposed back propagation neural network classifier that performs cross validation for original Neural Network. In order to optimize the classification accuracy, training time. Proposed web content mining methodology in the exploration with the aid of BPP. The main objective of this investigation is web document extraction and utilizing different grouping algorithm. This work extricates the data from the web URL.

Keywords


Back propagation algorithm, Clustering, Feature extraction, Information extraction, Information retrieval, Neural network, Steaming, Stop word

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DOI: http://doi.org/10.11591/ijai.v6.i2.pp74-78

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