Application of Clustering Filters in Order to Exclude Irrelevant Instances of the Process Before Using Reinforcement Learning to Optimize Business Processes in the Bank | Zendy

Andrey A. Bugaenko | Zendy

Open Access

Application of Clustering Filters in Order to Exclude Irrelevant Instances of the Process Before Using Reinforcement Learning to Optimize Business Processes in the Bank

Author(s) -

Andrey A. Bugaenko

Publication year - 2021

Publication title -

türk bilgisayar ve matematik eğitimi dergisi

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.218

H-Index - 3

ISSN - 1309-4653

DOI - 10.17762/turcomat.v12i6.4158

Subject(s) - cluster analysis , reinforcement learning , computer science , process (computing) , order (exchange) , reinforcement , business process , artificial intelligence , data mining , machine learning , work in process , engineering , programming language , operations management , structural engineering , finance , economics

The research offers and describes the use of clustering filters in order to exclude preliminarily the instances of the process, which contain errors, and which are not related directly with the business process, and, accordingly, are irrelevant for the analysis. Comparison of 15 types of filters was performed using mapped-out data. It was shown that successful preliminary filtering is possible before the application of reinforcement learning for business process analysis, which reduces the data processing amount.

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