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Statistical data mining and its appl...
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The University of Regina (Canada).
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Statistical data mining and its applications in health claims.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Statistical data mining and its applications in health claims./
作者:
Liu, Hui.
面頁冊數:
83 p.
附註:
Source: Masters Abstracts International, Volume: 47-02, page: 1039.
Contained By:
Masters Abstracts International47-02.
標題:
Computer Science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR42403
ISBN:
9780494424032
Statistical data mining and its applications in health claims.
Liu, Hui.
Statistical data mining and its applications in health claims.
- 83 p.
Source: Masters Abstracts International, Volume: 47-02, page: 1039.
Thesis (M.Sc.)--The University of Regina (Canada), 2008.
Data mining has become a commonly used tool for different fields. Statistics has played an important role in data mining, from data exploration to analysis. Not only are its theories used as solid foundation, but its methods are integrated into data mining.
ISBN: 9780494424032Subjects--Topical Terms:
626642
Computer Science.
Statistical data mining and its applications in health claims.
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Data mining has become a commonly used tool for different fields. Statistics has played an important role in data mining, from data exploration to analysis. Not only are its theories used as solid foundation, but its methods are integrated into data mining.
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In this thesis, statistics and its application in data mining are discussed, such as statistics models of mixture model, regression, Bayesian theorem, along with methods based on them, such as mixture model based clustering, Bayesian theorem classifier, and regression based anomaly detection.
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Data mining projects in Saskatchewan Public Health claims are used to demonstrate statistical methods. Furthermore, results from statistical models are compared with results from other methods. It is found that although statistics models have their unique advantages, they are also subject to some limitations. Therefore, combining statistical methods with other methods can yield more robust results.
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