國立虎尾科技大學 |

Mixture Models With Grouping Structure : = Retail Analytics Applications.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Mixture Models With Grouping Structure :/
其他題名:	Retail Analytics Applications.
作者:	Almohri, Haidar.
面頁冊數:	1 online resource (106 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355826906

Mixture Models With Grouping Structure : = Retail Analytics Applications.
Almohri, Haidar.

Mixture Models With Grouping Structure :Retail Analytics Applications. - 1 online resource (106 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--Wayne State University, 2018.

Includes bibliographical references

Growing competitiveness and increasing availability of data is generating tremendous interest in data-driven analytics across industries. In the retail sector, stores need targeted guidance to improve both the efficiency and effectiveness of individual stores based on their specific location, demographics, and environment. We propose an effective data-driven framework for internal benchmarking that can lead to targeted guidance for individual stores. In particular, we propose an objective method for segmenting stores using a model-based clustering technique that accounts for similarity in store performance dynamics. It relies on effective Finite Mixture of Regression (FMR) techniques for carrying out the model-based clustering with grouping structure ('must-link' constraints) and modeling store performance. We propose two alternate methods for FMR with grouping structure: 1) Competitive Learning (CL) and 2) Expectation Maximization (EM). The CL method can support both linear and non-linear regression methods whereas the more effective proposed EM approach only supports linear regression.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355826906Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Mixture Models With Grouping Structure : = Retail Analytics Applications.
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500 $a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
500 $a Adviser: Ratna Babu Chinnam.
502 $a Thesis (Ph.D.)--Wayne State University, 2018.
504 $a Includes bibliographical references
520 $a Growing competitiveness and increasing availability of data is generating tremendous interest in data-driven analytics across industries. In the retail sector, stores need targeted guidance to improve both the efficiency and effectiveness of individual stores based on their specific location, demographics, and environment. We propose an effective data-driven framework for internal benchmarking that can lead to targeted guidance for individual stores. In particular, we propose an objective method for segmenting stores using a model-based clustering technique that accounts for similarity in store performance dynamics. It relies on effective Finite Mixture of Regression (FMR) techniques for carrying out the model-based clustering with grouping structure ('must-link' constraints) and modeling store performance. We propose two alternate methods for FMR with grouping structure: 1) Competitive Learning (CL) and 2) Expectation Maximization (EM). The CL method can support both linear and non-linear regression methods whereas the more effective proposed EM approach only supports linear regression.
520 $a We also propose an optimization framework to derive tailored recommendations for individual stores within store clusters that jointly improves profitability for the store while also improving sales to satisfy franchiser requirements. We validate the methods using synthetic experiments as well as a real-world automotive dealership network study for a leading global automotive manufacturer.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
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