Today Network is one of the very important parts of life and a lot of essential activities are performed using network. Network security plays critical role in real life situations. This paper presents a Data Mining method in which various preprocessing methods are involved such as Normalization, Discretization and Feature selection. With the help of these methods the data is preprocessed and required features are selected. Here NaIve Bayes classifier is used in supervised learning method which classifies various network events for the KDD cup'99 Dataset. This dataset is the most commonly used dataset for Intrusion Detection.
The internet revolution has brought about a new way of expressing an individual's opinion. It has become a medium through which people openly express their views on various subjects. These opinions contain useful information which can be utilized in many sectors which require constant customer feedback. Analysis of the opinion and it's classification into different sentiment classes is gradually emerging as a key factor in decision making. There has been extensive research on automatic text analysis for sentiments such as sentiment classifiers, affect analysis, automatic survey analysis, opinion extraction, or recommender systems. These methods typically try to extract the overall sentiment revealed in a sentence or document, either positive or negative, or somewhere in between. However, a drawback of these methods is that the information can be degraded, especially in texts where a loss of information can also occur. The proposed method attempts to overcome the problem of the loss of text information by using well trained training sets. Also, recommendation of a product or request for a product as per the user's requirements have achieved with the proposed method.
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