Clustering Algorithms and RFM Analysis Performed on Retail Transactions

Yash Parikh

19 citations0/0 files storedIEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Customer churn and segmentation Advanced Clustering Algorithms Research Data Mining and Machine Learning Applications

Abstract

This paper investigates how clustering algorithms and Recency, Frequency, and Monetary value (RFM) analysis can be performed on online transactions to provide strategies for customer purchasing behaviors. Along with performing RFM analysis on the retail dataset, clustering algorithms such as Mean-shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Agglomerative Clustering, and K-Means were utilized. By comparing these clustering algorithms, we have found valuable customer groups based on RFM values.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 19 citations exist upstream.

References

2005RFM and CLV: Using Iso-Value Curves for Customer Base Analysis 2012Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining 2014A decision-making framework for precision marketing 2015Customer segmentation in a large database of an online customized fashion business 2016Customer Clustering Using a Combination of Fuzzy C-Means and Genetic Algorithms

Cited by

2023Imperative role of customer segmentation technique for customer retention using machine learning techniques 2024Analisis Loyalitas Pelanggan Business To Business Berdasarkan Model RFM Menggunakan Algoritma Fuzzy C-Means

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.