Identification of k-Most Promising Features to Set Blue Ocean Strategy in Decision Making

Identification of k-Most Promising Features to Set Blue Ocean Strategy in Decision Making

Abstract Customers demand typical type of products with multiple features. We want to develop a business intelligence system which helps the company to set the blue ocean strategy by discovering k-most promising features (k-MPF) from the customers’ query and a set of existing products of the similar...

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Bibliographic Details
Main Authors: Ritesh Kumar, Partha Sarathi Bishnu
Format: Article
Language:English
Published: SpringerOpen 2019-10-01
Series:Data Science and Engineering
Subjects:
Business intelligence
Market entry
Data mining
Decision making
Online Access:http://link.springer.com/article/10.1007/s41019-019-00106-z
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spelling doaj-0d3ff74d86944e328f1ba59ee3db34562021-04-02T12:53:02ZengSpringerOpenData Science and Engineering2364-11852364-15412019-10-014436738410.1007/s41019-019-00106-zIdentification of k-Most Promising Features to Set Blue Ocean Strategy in Decision MakingRitesh Kumar0Partha Sarathi Bishnu1Cambridge Institute of TechnologyBirla Institute of TechnologyAbstract Customers demand typical type of products with multiple features. We want to develop a business intelligence system which helps the company to set the blue ocean strategy by discovering k-most promising features (k-MPF) from the customers’ query and a set of existing products of the similar type. In this paper, we have formulated k-MPF to set the blue ocean strategy with compatible features. We have experimented with our proposed algorithms using different synthetic and real datasets, and the results showed the effectiveness of our proposed algorithms.http://link.springer.com/article/10.1007/s41019-019-00106-zBusiness intelligenceMarket entryData miningDecision making
collection DOAJ
language English
format Article
sources DOAJ
author Ritesh Kumar
Partha Sarathi Bishnu
spellingShingle Ritesh Kumar
Partha Sarathi Bishnu
Identification of k-Most Promising Features to Set Blue Ocean Strategy in Decision Making
Data Science and Engineering
Business intelligence
Market entry
Data mining
Decision making
author_facet Ritesh Kumar
Partha Sarathi Bishnu
author_sort Ritesh Kumar
title Identification of k-Most Promising Features to Set Blue Ocean Strategy in Decision Making
title_short Identification of k-Most Promising Features to Set Blue Ocean Strategy in Decision Making
title_full Identification of k-Most Promising Features to Set Blue Ocean Strategy in Decision Making
title_fullStr Identification of k-Most Promising Features to Set Blue Ocean Strategy in Decision Making
title_full_unstemmed Identification of k-Most Promising Features to Set Blue Ocean Strategy in Decision Making
title_sort identification of k-most promising features to set blue ocean strategy in decision making
publisher SpringerOpen
series Data Science and Engineering
issn 2364-1185
2364-1541
publishDate 2019-10-01
description Abstract Customers demand typical type of products with multiple features. We want to develop a business intelligence system which helps the company to set the blue ocean strategy by discovering k-most promising features (k-MPF) from the customers’ query and a set of existing products of the similar type. In this paper, we have formulated k-MPF to set the blue ocean strategy with compatible features. We have experimented with our proposed algorithms using different synthetic and real datasets, and the results showed the effectiveness of our proposed algorithms.
topic Business intelligence
Market entry
Data mining
Decision making
url http://link.springer.com/article/10.1007/s41019-019-00106-z
work_keys_str_mv AT riteshkumar identificationofkmostpromisingfeaturestosetblueoceanstrategyindecisionmaking
AT parthasarathibishnu identificationofkmostpromisingfeaturestosetblueoceanstrategyindecisionmaking
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