In this paper, we introduce two independent hybrid mining algorithms to improve the classification accuracy rates of decision tree (DT) and naïve Bayes (NB) classifiers for the classification of multi-class problems. ...

Traditional data mining techniques cannot be directly applied to the real-time data streaming environment. Existing mining classifiers therefore need to be updated frequently to adopt the changes in data streams. In this ...

In this paper, we introduce a new learning algorithm for adaptive intrusion detection using boosting and naïve Bayesian classifier, which considers a series of classifiers and combines the votes of each individual classifier ...

In this paper a neural network based image compression method is presented. Neural networks offer the potential for providing a novel solution to the problem of data compression by its ability to generate an internal data ...

Recently, information security has become a key issue in information technology as the number of computer security breaches are exposed to an increasing number of security threats. A variety of intrusion detection systems ...

A classification paradigm is a data mining framework containing all the concepts extracted from the training dataset to differentiate one class from other classes existed in data. The primary goal of the classification ...

This paper is concerned with verification of signatures using feature analysis and non linear classifier. Signatures are collected and scanned to obtain input image. Preprocessing involves removal of noise and making the ...