Abstract: Difference and similarity are very important factor in clustering algorithms,and always affect the results of clustering analysis．A lot of clustering algorithms use Euclidean distance as it’s similarity measure．Euclidean distance can''t reflect the global information of attributes,and don''t consider the unit differences between each attribute,so it can’t make a good result when there is obvious unit and domain differences．So,this paper put forward a generally weighted Minkowski distance which is determined by the unit and domain information of each attributes value．Not only characteristics of whole data are considered,but also dicord between attributes is removed,at the same time,using of fractional bits weakens the noise data influence．We used new distance measure in classic k-means.And quantum genetic k-means and the experimental result show that the new algorithm is effective.

Key words: Data clustering,Minkowski distance,Fractional bits,Global information,QGA