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Cluster Computing

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01-12-2016

The application of terms mining technique to clustering participant’s character patterns in the enterprise management

Authors: Shuwei Jing, Zhanwen Niu, Chun-Bin Tung, Zih-Ping Ho

Published in: Cluster Computing | Issue 4/2016

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Abstract

The participant’s character patterns are different in different progresses of enterprise management innovation at various stages of advanced lean management. Term mining technique is used to analyze the text of the results of interviews to detect participant’s character patterns in the enterprise management innovation process (EMIP). Five levels of fuzzy language variables—very low, low, normal, high and very high, are used to differentiate the participant’s character patterns in the EMIP at the stages of advanced lean management of point, line, plane and cube. The main conclusions from this research are: (1) Participant’s character patterns in the EMIP are developed from the four stages (point, line, panel, cube)of the advanced lean management, providing the gist for more targeted designing of the promotion mechanism in the progress of advanced enterprise lean management. (2) The application of text mining method can be extended to analyze participant’s character patterns. (3) Get the participant’s character patterns can provide the basis for company management.

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Title: The application of terms mining technique to clustering participant’s character patterns in the enterprise management
Authors: Shuwei Jing
Zhanwen Niu
Chun-Bin Tung
Zih-Ping Ho
Publication date: 01-12-2016
Publisher: Springer US
Published in: Cluster Computing / Issue 4/2016
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-016-0654-x

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