Challenges and Solutions for Manufacturing Products Using Advanced Enterprise Data Mining Approaches

Objectives: The major intention of this investigation is to increase the accuracy of manufacturing enterprises using efficient data mining techniques. This scenario also intends to accomplish the higher performances by reducing the challenges and producing the potential solutions.

Methods: In this research, k-nearest neighbor (kNN), Naïve bayes (NB), C4.5 and J48 algorithms are used to evaluate the medical database, transactional database and agricultural database. This scenario considers the feature extraction and feature selection to improve the accuracy of system.

Findings: The algorithms are used in this work namely kNN, NB, C4.5 and J48 algorithms are used to improve the manufacturing performances. The experimental results are shown that the J48 classification algorithm is superior to other algorithms such as kNN, NB and C4.5. These techniques are improved in terms of all performance metrics called accuracy, precision and recall values.

Application/Improvements: The findings demonstrate that accuracy performance is improved significantly by using J48 algorithm in this scenario. The data mining algorithms are used to reduce the challenges tremendously and provide optimal solutions for manufacturing enterprises.