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Analysis of Sales data for the Semiconductor using Data mining
데이터마이닝을 이용한 반도체 판매데이터 분석

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Authors
최윤영
Advisor
김용대
Major
자연과학대학 통계학과
Issue Date
2014-08
Publisher
서울대학교 대학원
Keywords
clustering methodinverse covariancepartial correlation estimationhub network
Description
학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2014. 8. 김용대.
Abstract
Increased demand for products which are Smartphone, tabletPC and other mobile device using Mobile DRAM over the world makes sales increase of Mobile DRAM. This paper suggests valid statistical methods for application of sales data and analyzes the relation and trend among the type of Mobile DRAM, density and sales area. In addition, we could get another new idea via the result. For analysis, Clustering, logistic regression with lasso, decision tree and Partial Correlation Estimation method are introduced. glasso (graphic lasso) that is algorithm to estimate a sparse inverse covariance matrix using lasso penalty (L1 penalty) is used for partial correlation estimation and then, hub network graph is made by space (Sparse Partial Correlation Estimation) and available to be used for better decision making and developing a strategy in Marketing and Sales.
Language
English
URI
https://hdl.handle.net/10371/131288
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Master's Degree_통계학과)
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