MULTI-LABEL CLASSIFICATION should be done yeast data(Yeast data contains 104 attributes and 14 labels) in two steps- 1)Traning phase 2)Testing phase. In training phase, first data should be read and next, it should be clustered using K-means algorithm. after that data should be normalized and [url removed, login to view] is to apply apriori algorithm and obtains rules between attributes and labels and using FP Growth mining algorithm we need to find the rules between attributes. Next is the testing phase we have to test the data. finally, the efficiency of the process should be measured by hamming loss technique. I need this whole implementation in python at low cost and less time or I have the who Multi-label classification using apriori algorithm implemented in [url removed, login to view] if anyone good in Java I will share that files and you can just implement fp growth algorithm and add to the previous project.