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– Classification in data mining has gained a lot of importance in literature and it has a great deal of application areas from medicine to astronomy, from banking to text classification.. It can be described as supervised learning algorithm as it assigns class labels to data objects based on the relationship between the data items with a pre-defined class(More)
The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. Neural networks are commonly used networks in many engineering applications due to its better generalization property. An ensemble neural network algorithm is proposed based on the Akaike information criterion (AIC).(More)
Data warehousing technology has made a huge impact in the world of business; it helps to turn data into information that helps analysts to make strategic decisions. Currently most data warehouse approaches employ static refresh mechanisms. But for various business requirements this is not an appropriate solution. Some critical data need to be refreshed in(More)
A drawback of the error-back propagation algorithm for a multilayer feed forward neural network is over learning or over fitting. We have discussed this problem, and obtained necessary and sufficient Experiment and conditions for over-learning problem to arise. Using those conditions and the concept of a reproducing, this paper proposes methods for choosing(More)
Association rule mining is a powerful model of data mining used for finding hidden patterns in large databases. One of the great challenges of data mining is to protect the confidentiality of sensitive patterns when releasing database to third parties. Association rule hiding algorithms sanitize database such that certain sensitive association rules cannot(More)
In today's fast-changing, competitive environment, a complaint frequently heard by data warehouse users is that access to time-critical data is too slow. Shrinking batch windows and data volume that increases exponentially are placing increasing demands on data warehouses to deliver instantly-available information. Additionally, data warehouses must be able(More)
The goal of classification learning is to develop a model that separates the data into the different classes, with the aim of classifying new examples in the future. A weak learner is one which takes labeled training examples and produces a classifier which can label test examples more accurately than random guessing. When such weak learner is used directly(More)
The Artificial neural networks are relatively crude electronic networks of "neurons" based on the neural structure of the brain. It process the records one at a time, and "learn" by comparing their prediction of the record with the known actual record. The errors from the initial prediction of the first record is fed back into the network, and used to(More)
Many approaches in constraint based sequential pattern mining have been proposed and most of them focus only on the concept of frequency, which means, if a pattern is not frequent, it is removed from further consideration. Frequency is a good indicator of the importance of a pattern but in real life, however, the environment may change constantly and(More)
— an Artificial Neural Network classifier is a nonparametric classifier. It does not need any priori knowledge regarding the statistical distribution of the class in a giver selected data Source. While, neural network can be trained to distinguish the criteria used to classify easily in a generalized manner that allows successful classification the newly(More)