• Dharmendra Sharma

Enormous amount of information is available over the internet in electronic format but collecting and cataloging the relevant information is very challenging task. The large amount of data can be analyzed to optimize the benefits, for business intelligent system. The storage and retrieval of information is an important and extensively studied problem in machine learning. The basic steps in information retrieval include preprocessing of document, extracting relevant terms against the features in a database, and finally categorizing a set of documents into predefined categories. In this paper we are had use two preprocessing activity as stop word removal and stemming and evaluated the impact of stop word and stemming onto feature selection. From the results we find that the removal of stop-words decrease the size of feature set. For sparsity value 0.9 it decrease by 9%.On the other hand for stemming process as we increase the sparsity value the size of feature set also increase.

Information Retrieval, Term, and learning, stemming, feature selection

"Evaluation of Preprocessing Techniques for Information Storage and Retrieval System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 8, page no.966-968, August-2018, Available :http://www.jetir.org/papers/JETIR1808625.pdf

"Evaluation of Preprocessing Techniques for Information Storage and Retrieval System ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 8, page no. pp966-968, August-2018, Available at : http://www.jetir.org/papers/JETIR1808625.pdf