Standard Additive Model in Data Mining

Abstract

The habitual purpose of data mining is prediction, one of the most direct real-world applications. There are many technologies available to data mining in literature and they achieved some results with reasonable accuracies. This paper designs and implements an advanced model based on fuzzy inference system, namely Standard Additive Model (SAM) for forecasting the output of any record given the input variables only from the database, the age of abalone in particular. SAM offers an optimum solution for the prediction and can be definitely an alternative approach for conventional models such as neural networks. The experimental result comparison to multi-layer perceptron neural network (MLPNN) is provided in same context.