Maintenance Supervision of the Dies Condition and Technological Quality of Forged Products in Industrial Conditions

• Autorzy: Juhi Singh , Mandeep Mittal
• Języki publikacji: EN

Abstrakty

EN

Traditional clustering algorithms which use distance between a pair of data points to calculate their similarity are not suitable for clustering of boolean and categorical attributes. In this paper, a modified clustering algorithm for categorical attributes is used for segmentation of customers. Each segment is then mined using frequent pattern mining algorithm in order to infer rules that helps in predicting customer's next purchase. Generally, purchases of items are related to each other, for example, grocery items are frequently purchased together while electronic items are purchased together. Therefore, if the knowledge of purchase dependencies is available, then those items can be grouped together and attractive offers can be made for the customers which, in turn, increase overall profit of the organization. This work focuses on grouping of such items. Various experiments on real time database are implemented to evaluate the performance of proposed approach. (original abstract)

Słowa kluczowe

EN

Algorithms; Cluster conception; Relationships with customers

PL

Algorytmy; Koncepcja klastrów; Relacje z klientami

• Czasopismo: Management and Production Engineering Review
• Rocznik: 2021
• Tom: 12
• Numer: nr 2
• Strony: 57--64

Twórcy

autor

Juhi Singh

• Amity School of Engineering and Technology, India

autor

Mandeep Mittal

• Amity University Uttar Pradesh, India

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Identyfikatory

DOI

10.24425/mper.2021.137678