Concentration of scores and distances: derived values such as distances become numerically similar Irrelevant attributes: in high dimensional data, a significant number of attributes may be irrelevant Definition of reference sets: for local methods, reference sets are often nearest-neighbor based Incomparable scores for different dimensionalities: different subspaces produce incomparable scores Interpretability of scores: the scores often no longer convey a semantic meaning Exponential search space: the search space can no longer be systematically scanned Data snooping bias: given the large search space, for every desired significance a hypothesis can be found Hubness: certain objects occur more frequently in neighbor lists than others.