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A tree-based summarization framework...
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Kent State University.
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A tree-based summarization framework for differences between two data sets.
Record Type:
Electronic resources : Monograph/item
Title/Author:
A tree-based summarization framework for differences between two data sets./
Author:
Wang, Dong.
Description:
51 p.
Notes:
Adviser: Ruoming Jin.
Contained By:
Masters Abstracts International47-03.
Subject:
Computer Science. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=1461526
ISBN:
9780549970736
A tree-based summarization framework for differences between two data sets.
Wang, Dong.
A tree-based summarization framework for differences between two data sets.
- 51 p.
Adviser: Ruoming Jin.
Thesis (M.S.)--Kent State University, 2009.
This work addresses the issue of describing the difference between two data sets. A framework is developed to quantify the difference between two data sets, given that the difference is induced by the different statistical distributions of the two data sets. Besides the quantification, this framework also provides an intuitive explanation of difference: a decision tree like structure is built to interpret the interesting point(s) of the difference. A dynamic programming algorithm is developed to give the global optimal solution. However, it has high computational complexity. To improve the efficiency, a greedy algorithm is proposed. Both algorithms are tested against the synthetic data sets and the real data sets.
ISBN: 9780549970736Subjects--Topical Terms:
626642
Computer Science.
A tree-based summarization framework for differences between two data sets.
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51 p.
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Adviser: Ruoming Jin.
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Source: Masters Abstracts International, Volume: 47-03, page: 1711.
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Thesis (M.S.)--Kent State University, 2009.
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This work addresses the issue of describing the difference between two data sets. A framework is developed to quantify the difference between two data sets, given that the difference is induced by the different statistical distributions of the two data sets. Besides the quantification, this framework also provides an intuitive explanation of difference: a decision tree like structure is built to interpret the interesting point(s) of the difference. A dynamic programming algorithm is developed to give the global optimal solution. However, it has high computational complexity. To improve the efficiency, a greedy algorithm is proposed. Both algorithms are tested against the synthetic data sets and the real data sets.
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http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=1461526
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