• A. P. Tawdar
• M. S. Bewoor
• Prof. Dr. S. H. Patil

Text and Document classification has gained a tremendous importance due to huge amount of information stored in databases and web user’s dependency on the search engine’s to retrieve relevant information. Text Classification also known as Text Categorization is the task of automatically classifying a set of text documents into different categories from a predefined set. Text Classification helps in quick and relevant information retrieval from relational databases, documents, text, multimedia files, and World Wide Web. The applications are wide and not limited to only text summarization, search engines, document clustering and spam filtering. 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.

Back Propagation Algorithm, Neural Network, Information Retrieval, Information Extraction, Clustering, Steaming, Stop word, Feature Extraction.

"Web Document Mining using Incremental Approach of Neural Network in BPP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.3, Issue 11, page no.65-68, November-2016, Available :http://www.jetir.org/papers/JETIR1611010.pdf

"Web Document Mining using Incremental Approach of Neural Network in BPP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.3, Issue 11, page no. pp65-68, November-2016, Available at : http://www.jetir.org/papers/JETIR1611010.pdf