Algorithm Of KDD And Its Application In Sensory Evaluation

KDD is a technology, which uses computer and combines artificial intelligence Statistics and computer with database and other technologies to extract new information from data. Sensory evaluation has subjective and agilitive characters, which once depended on experts'experiences. With information expanding, industry demands increasing and improve of evaluation precision, traditional methods have hardly been spreaded extensively. So using neural networks, statistics and data extraction to realize intelligent sensory evaluation for KDD will be important and significant.Neural networks have characteristics of nonlinear mapping, self-learning and generalization. However, the knowledge extracted from trained networks is hardly expressed in formulas. So it is difficult to understand. Firstly, neural networks pruning method can achieve inputs dimension compression and feature extraction. The activative function of the hidden unitis then approximated by a three-piece linear function. This method can apply for tobacco sensory evaluation to extract input indexes which are more correlative with output indexes and the piecewise linear rules. But this method has some deficiencies. In order to make the extracted rule and knowledge more visual and precise, M5 method is then introduced. After analyzing the principle and flow of the arithmetic, application in tobacco sensory evaluation for KDD is achieved. Finally, statistics, neural networks and M5 method are comparaed and combined to design a solution project of KDD for tobacco sensory evaluation. The project has not only been validated by experiences but also confirmed by practice. It has also been accepted by a company and helped exports apply for sensory evaluation.