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Classification is an important problem in data mining. Given a database of records, each with a class label, a classifier generates a concise and meaningful description for each class that can be used to classify subsequent records. A number of popular classifiers construct decision trees to generate class models. These classifiers first build a decision tree and then prune subtrees from the decision tree in a subsequent pruning phase to improve accuracy and prevent "overfitting". In this paper, the different pruning methodologies available & their various features are discussed. Also the effectiveness of pruning is evaluated in terms of complexity and classification accuracy by applying C4.5 decision tree classification algorithm on Credit Card Database with pruning and without pruning. Instead of classifying the transactions either fraud or non-fraud the transactions are classified in four risk levels which is an innovative concept.

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No abstract

In this paper, a novel watermarking technique is proposed for data authentication and integrity of Relational Database. For integrity verification of tables in the database, the watermark has to depend on a secret key and on the original copy of that table. It is important that the dependence on the key should be sensitive. The proposed method makes use of the concept of eigen values by constructing a tuple -Relation matrix for each tuple. The eigen values are used for generating the watermark for a record in the table. Watermark embedding is done by using eigen values in a non numeric attribute of a tuple. Detection of the watermark prove the authenticate and integrity of data. We will show that our approach leads to an effective technique that is robust against different forms of malicious attacks as well as benign updates to the data. General TermsSecurity