Based　on　a　data-driven　selection　of　an　estimator　from　a　fixed　family　of　kernel　estimators,　Goldenshluger　and　Lepski　(Probab　Theory　Relat　Fields　159:479-543,　2014)　considered　the　problem　of　adaptive　min-imax　un-compactly　supported　density　estimation　on　R-d　with　L-p　risk　over　Nikol＇skii　classes.　This　paper　shows　the　same　convergence　rates　by　using　a　data-driven　wavelet　estimator　over　Besov　spaces,　because　the　wavelet　estimations　provide　more　local　information　and　fast　algorithm.　Moreover,　we　explore　better　convergence　rates　under　the　independence　hypothesis,　which　reduces　the　dimension　disaster　effectively.