Abstract
In this paper, we discuss the characteristic of incomplete data, and suggest the principle of information diffusion to deal optimally with them to recognize the concerning relationships. The basic function of the principle is to expand a crisp observation so as to stuff the gap caused by lacking data. Using the fuzziness of incomplete data, we prove the principle. Properties of information diffusion estimator on probability density function confirm that the principle is true. We discuss two simple diffusion methods: the information distribution and the normal information diffusion method. We also study two practical problems, which are risk assessment and recognition of isoseismal area, to show the advantages of the information diffusion.
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