Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Vijayalakshmi, S.; Gayathri, S.P. Title A name given to the resource An Enhanced RFM Customer Value-Based Customer Segmentation and Evaluation Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1362 LNNS;pp.511-523 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-5214-3_41" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5214-3_41</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015664535?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015664535?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vijayalakshmi S., CHRIST University, Bengaluru, India; Gayathri S.P., Government Arts College for Women, Tamil Nadu, Nilakottai, India Description An account of the resource Machine Learning Algorithms are widely used in the contemporary era of highly compatible technical improvements to provide answers to the challenges of business environment, yet crucial services for a firm to run successfully in this intensely competitive E-commerce sector. Recently, strategies like clustering and classification mechanisms that allow for the classification of both existing and new clients into clusters have also produced positive outcomes. Recency, Frequency, and Monetary (RFM) measures are hugely being used these days to perform these kinds of tasks. In this study, individual one-dimensional clustering on the Recency, Frequency, and Monetary columns was performed, and a weighted average or preferred linear combination of the three features was then used to calculate an overall score. Summing up the result of three individual clusters. Finally, all of the distinct clients were divided into these three segments based on the overall score, which was divided into three categories. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Classification; Clustering; Machine learning; RFM Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981965213-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online