Abstract
ABSTRACT Nowadays, the capability to both generate and collect data has been expanded enormously and provides us with huge amount of data. Millions of databases have been used in business data management, scientific and engineering data management, and many other applications. It is noted that the number of such databases keeps growing rapidly because of the availability of powerful and affordable database management systems. To compete effectively in such an environment, business managers must take advantage of high-return opportunities in time. But however, the difficulty of discerning the value of information keeps many companies from capitalizing fully on the wealth of data at their disposal. Data mining is the art of finding patterns in data and is a new approach based on a general recognition that there is undraped value in large databases and utilities data-driven extraction of information. However, it is still not easy to identify the complicate relationship in the huge data set. Moreover, in most case, the estimation of parameters or the classification results cannot really describe the realization of business modeling. The artificial neural network is becoming a very popular alternative in prediction and classification task due to its associated memory characteristic and generalization capability. The objective of the proposed study is to explore the performance of data classification by integrating the backpropagation neural networks with discriminant analysis approach. To demonstrate the inclusion of the classification result from the discriminant analysis would improve the classification accuracy of the network, classification tasks are performed on two data sets, Iris data and one practical bank data. As the results reveal, the proposed integrated approach converges much faster than the conventional neural network. Moreover, the classification accuracies increase for both cases in terms of the proposed methodology.
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