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Information Journal Paper

Title

Framework of Using Data Mining for Business Process Improvment

Pages

  25-45

Abstract

 Organizations should consider Process Improvement for promoting their performance. The main problem is the high volume of process dataset along with the wide variety of their features that leads to increase the complexity in Process Improvement methods. Previous methods cannot improve processes in a high volume of process dataset. Data Mining approach can support improvement methods by identifying valuable hidden patterns in high volume of process dataset. In this paper a framework of using Data Mining techniques for extracting valuable hidden patterns in high volume of process dataset is developed for presenting the improvement suggestions. In order to evaluate the proposed framework, a real set of processes along with their features were gathered. After that, using classification, clustering, and features selection algorithms, valuable patterns in high volume of process dataset were identified. After evaluating these patterns, improvement suggestions were recommended by the identified patterns. The results showed that identified patterns can support processes improvement activities with recommending the improvement suggestions.

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  • Cite

    APA: Copy

    KHANBABAEI, MOHAMMAD, MOVAHEDI SOBHANI, FARZAD, ALBORZI, MAHMOOD, & RADFAR, REZA. (2019). Framework of Using Data Mining for Business Process Improvment. PRODUCTION AND OPERATIONS MANAGEMENT, 10(1 (18) ), 25-45. SID. https://sid.ir/paper/217623/en

    Vancouver: Copy

    KHANBABAEI MOHAMMAD, MOVAHEDI SOBHANI FARZAD, ALBORZI MAHMOOD, RADFAR REZA. Framework of Using Data Mining for Business Process Improvment. PRODUCTION AND OPERATIONS MANAGEMENT[Internet]. 2019;10(1 (18) ):25-45. Available from: https://sid.ir/paper/217623/en

    IEEE: Copy

    MOHAMMAD KHANBABAEI, FARZAD MOVAHEDI SOBHANI, MAHMOOD ALBORZI, and REZA RADFAR, “Framework of Using Data Mining for Business Process Improvment,” PRODUCTION AND OPERATIONS MANAGEMENT, vol. 10, no. 1 (18) , pp. 25–45, 2019, [Online]. Available: https://sid.ir/paper/217623/en

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