Maybe these are just language issues but I think they are worth clarifying.
Firstly you state missing values with less than 10% missing are ignored. This is not completely true. If less than 10% are missing (and data is scarce and therefore precious), the first approach people employ is to try and impute missing values. If you drop away each input with even one missing value you might end up dropping a lot of the entries. Points are generally only dropped when a lot of their attributes are missing
You state "Normal Statistical Models cannot be used...". This is not completely true. A lot of the imputation techniques end up employing EM techniques with Normal update steps. In a lot of the techniques, missing values are given the same dummy label of (say -1). I would not do this in the case of clustering techniques because it has the possibility of messing up your distance calculations.