e-ISSN : 0975-3397
Print ISSN : 2229-5631
Home | About Us | Contact Us

ARTICLES IN PRESS

Articles in Press

ISSUES

Current Issue
Archives

CALL FOR PAPERS

CFP 2021

TOPICS

IJCSE Topics

EDITORIAL BOARD

Editors

Indexed in

oa
 

ABSTRACT

Title : Text Analytics to Data Warehousing
Authors : Kusum bharti, Shweta Jain, Sanyam Shukla
Keywords : Feature selection, k-mean clustering, fuzzy k mean clustering, Random Forest, and KDDcup 99 dataset
Issue Date : September 2010
Abstract :
Due to continuous growth of the internet technology, there is need to establish security mechanism. So for achieving this objective various NIDS has been propsed. Datamining is one of the most effective techniques used for intrusion detection. This work evaluates the performance of unsupervised learning techniques over benchmark intrusion detection datasets. The model generation is computation intensive, hence to reduce the time required for model generation various feature selection algorithm has been used. Problems with k-mean clustering are hard cluster to class assignment, class dominance, and null class problems. From experimental results it is observed that for 2 class datasets filtered fuzzy random forest dataset gives the better results. It is having 99.2% precision and 100% recall, So it can be summarize that proposed statistical model is giving better performance better results than existing clustering algorithm.
Page(s) : 2197-2200
ISSN : 0975–3397
Source : Vol. 2, Issue.6

All Rights Reserved © 2009-2024 Engg Journals Publications
Page copy protected against web site content infringement by CopyscapeCreative Commons License