Customer’s Purchase Prediction Using Customer Segmentation Approach for Clustering of Categorical Data

• Autorzy: Singh Juhi , Mittal Mandeep

Identyfikatory

DOI

10.24425/mper.2021.137678

• 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.

Słowa kluczowe

EN

categorical data; clustering algorithm; frequent pattern mining; association rules; customer relationship management

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2021
• Tom: Vol. 12, No. 2
• Strony: 57--64
• Opis fizyczny: Bibliogr. 36 poz., tab., wykr.

Twórcy

autor

Singh Juhi

• Department of Computer Science, Amity School of Engineering and Technology, Delhi, India

autor

Mittal Mandeep

• Department of Mathematics, Amity Institute of Applied Sciences, Amity University Uttar Pradesh, Noida, India-201303, phone: [phone]

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)