Keywords: In this chapter, we will examine a general density-based approach for handling uncertain data. The broad idea is that implicit information about the errors can be indirectly incorporated into the density estimate. We discuss methods for constructing error-adjusted densities of data sets, and using these densities as intermediate representations in order to perform more accurate mining. We discuss the mathematical foundations behind the method and establish ways of extending it to very large scale data mining problems. As concrete examples of our technique, we show how to apply the intermediate density representation in order to accurately solve the classification and outlier detection problems. This approach has the potential in constructing intermediate representations as a broad platform for data mining applications.
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