Open AccessKnowledge Push Method Based on Clustering Algorithm and DSM Matrix
Author(s) -
Ding Wang,
Hongbing Zhan,
Jin Yu,
Rui Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2025/1/012057
Subject(s) - cohesion (chemistry) , computer science , cluster analysis , business process modeling , business rule , business process , artifact centric business process model , business process discovery , business analytics , business intelligence , data mining , business analysis , knowledge management , process management , artificial intelligence , business model , business , work in process , marketing , chemistry , organic chemistry
In the process of business execution of traditional enterprises, traditional knowledge push is difficult to provide accurate knowledge support for business execution decision-making due to the complicated business problems. Therefore, this paper proposes a knowledge-push model for business execution. Firstly, the business problems in the business process are clustered through the hierarchical clustering algorithm to ensure the cohesion of the business problems after clustering. Then DSM matrix is used to represent the coupling of clustered business problems. Finally, knowledge push is carried out on the divided business problems to provide accurate knowledge support for business executives to make business problems decisions.