Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40911
Title: Mining progressive confident rules
Authors: Zhang, M. 
Hsu, W. 
Lee, M.L. 
Keywords: Classification
Progressive confident
Sequence
Issue Date: 2006
Source: Zhang, M.,Hsu, W.,Lee, M.L. (2006). Mining progressive confident rules. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2006 : 803-808. ScholarBank@NUS Repository.
Abstract: Many real world objects have states that change over time. By tracking the state sequences of these objects, we can study their behavior and take preventive measures before they reach some undesirable states. In this paper, we propose a new kind of pattern called progressive confident rules to describe sequences of states with an increasing confidence that lead to a particular end state. We give a formal definition of progressive confident rules and their concise set. We devise pruning strategies to reduce the enormous search space. Experiment result shows that the proposed algorithm is efficient and scalable. We also demonstrate the application of progressive confident rules in classification. Copyright 2006 ACM.
Source Title: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
URI: http://scholarbank.nus.edu.sg/handle/10635/40911
ISBN: 1595933395
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

50
checked on Dec 9, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.