NDLI: Algorithms for discovering bucket orders from data

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Algorithms for discovering bucket orders from data

Content Provider	ACM Digital Library
Author	Mannila, Heikki Gionis, Aristides Puolamäki, Kai Ukkonen, Antti
Abstract	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.
Starting Page	561
Ending Page	566
Page Count	6
File Format	PDF
ISBN	1595933395
DOI	10.1145/1150402.1150468
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Bucket order Ranking Ordering Partial order
Content Type	Text
Resource Type	Article

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