document: Algorithms for discovering bucket orders from data Ordering and ranking items of different types are important tasks in various applications , such as query processing and scientific data mining . A total order for the items can be misleading , since there are groups of items that have practically equal ranks . We consider bucket orders , i.e. , total orders with ties . They can be used to capture the essential order information without overfitting the data : they form a useful concept class between total orders and arbitrary partial orders . We address the question of finding a bucket order for a set of items , given pairwise precedence information between the items . We also discuss methods for computing the pairwise precedence data . We describe simple and efficient algorithms for finding good bucket orders . Several of the algorithms have a provable approximation guarantee , and they scale well to large datasets . We provide experimental results on artificial and a real data that show the usefulness of bucket orders and demonstrate the accuracy and efficiency of the algorithms .
doc_bio_tags: O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O