8. A computer-implemented method for assessing a confidence level in an item categorization for an item of interest, the method comprising: under control of one or more configured computer systems; obtaining item information regarding an item of interest; obtaining categories for items; determining one or more relevance values representing a similarity of the item information to each of the obtained categories; selecting one or more of the obtained categories based upon their determined relevance value; assigning an obtained category as a first category for the item of interest based, at least in part, upon an analysis of the selected categories; determining a relevance clustering parameter based at least in part upon a pairwise difference between each of the determined relevance values of the selected categories; determining a category distance parameter representing a similarity of selected categories to one another; determining a relevance clustering classification characterizing the confidence level in the assigned first category, wherein: the relevance clustering classification is determined to be tight if the relevance clustering parameter is less than a first threshold value; and the relevance clustering classification is determined to be loose if the relevance clustering parameter is greater than the first threshold value; determining a category distance classification further characterizing the confidence level in the assigned first category, wherein: the category distance classification is determined to be close if the category distance parameter based upon the determined category distances of the selected categories is less than a second threshold value; and the category distance classification is determined to be far if the category distance parameter is greater than the second threshold value; and determining a confidence level in the assigned first category based upon the relevance clustering parameter, the category distance parameter, the relevance clustering classification, and the category distance classifications.
8. A computer-implemented method for assessing a confidence level in an item categorization for an item of interest, the method comprising: under control of one or more configured computer systems; obtaining item information regarding an item of interest; obtaining categories for items; determining one or more relevance values representing a similarity of the item information to each of the obtained categories; selecting one or more of the obtained categories based upon their determined relevance value; assigning an obtained category as a first category for the item of interest based, at least in part, upon an analysis of the selected categories; determining a relevance clustering parameter based at least in part upon a pairwise difference between each of the determined relevance values of the selected categories; determining a category distance parameter representing a similarity of selected categories to one another; determining a relevance clustering classification characterizing the confidence level in the assigned first category, wherein: the relevance clustering classification is determined to be tight if the relevance clustering parameter is less than a first threshold value; and the relevance clustering classification is determined to be loose if the relevance clustering parameter is greater than the first threshold value; determining a category distance classification further characterizing the confidence level in the assigned first category, wherein: the category distance classification is determined to be close if the category distance parameter based upon the determined category distances of the selected categories is less than a second threshold value; and the category distance classification is determined to be far if the category distance parameter is greater than the second threshold value; and determining a confidence level in the assigned first category based upon the relevance clustering parameter, the category distance parameter, the relevance clustering classification, and the category distance classifications. 27. The computer-implemented method of claim 8 , wherein the relevance clustering classification is determined to be tight and the category distance classification is determined to be far and further comprising: determining a common ancestor occurring in each of the selected categories; and assigning the common ancestor of the selected categories as a category of the item of interest.