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Benchmarking Clustering and Co-Clustering Methods for Return-Behavior Segmentation in UK E-Commerce: A Behavioral Reverse Logistics Perspective

https://doi.org/10.2139/ssrn.7391499
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Comparing High-Dimensional Partitions with the Co-clustering Adjusted Rand Index
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A tutorial on spectral clustering
inferredcites →https://doi.org/10.1007/s11634-023-00549-3openalex-snapshot
Co-clustering contaminated data: a robust model-based approach
inferredcites →https://doi.org/10.1016/j.ejor.2020.10.042openalex-snapshot
Forays into omnichannel: An online retailer’s strategies for managing product returns
inferredcites →https://doi.org/10.1016/j.ifacol.2025.09.487openalex-snapshot
Tracing Uncertainty in Reverse Logistics: A Decision Support System for Zero Defect Remanufacturing Quality and Quantity Control
inferredcites →https://doi.org/10.1016/j.ijpe.2011.01.005openalex-snapshot
An empirical analysis on the influence of risk on relationships between handling of product returns and customer loyalty in E-commerce
inferredcites →https://doi.org/10.1016/j.jom.2012.02.002openalex-snapshot
The customer consequences of returns in online retailing: An empirical analysis
inferredcites →https://doi.org/10.1016/j.jretai.2016.03.001openalex-snapshot
To Keep or Not to Keep: Effects of Online Customer Reviews on Product Returns
inferredcites →https://doi.org/10.1016/j.jretai.2021.05.001openalex-snapshot
Information Search and Product Returns Across Mobile and Traditional Online Channels
inferredcites →https://doi.org/10.1038/s41598-020-66848-3openalex-snapshot
Identification of cell types from single cell data using stable clustering
inferredcites →https://doi.org/10.1080/23311916.2020.1865598openalex-snapshot
Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach
inferredcites →https://doi.org/10.1093/bib/bbae342openalex-snapshot
Biclustering data analysis: a comprehensive survey
inferredcites →https://doi.org/10.1108/09574090210806397openalex-snapshot
The Returns Management Process
inferredcites →https://doi.org/10.1108/ijpdlm-01-2015-0010openalex-snapshot
Online retail returns management
inferredcites →https://doi.org/10.1108/ijpdlm-02-2013-0020openalex-snapshot
Customer segmentation based on buying and returning behaviour
inferredcites →https://doi.org/10.1109/tsp.2014.2360141openalex-snapshot
Soft Nonnegative Matrix Co-Factorization
inferredcites →https://doi.org/10.1111/deci.12625openalex-snapshot
Order basket contents and consumer returns
inferredcites →https://doi.org/10.1111/jbl.12273openalex-snapshot
On packaging and product returns in online retail—Mailing boxes or sending signals?
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Co-clustering: A Survey of the Main Methods, Recent Trends, and Open Problems
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Co-clustering documents and words using bipartite spectral graph partitioning
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Are Product Returns a Necessary Evil? Antecedents and Consequences
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Performance Evaluation of Cheng & Church (CC) and Spectral Biclustering Algorithms under Collinearity and Overlap Conditions
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Customer Segmentation With Machine Learning for Online Retail Industry
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Analysis of Elbow, Silhouette, Davies-Bouldin, Calinski-Harabasz, and Rand-Index Evaluation on K-Means Algorithm for Classifying Flood-Affected Areas in Jakarta
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Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification
inferredcites →https://doi.org/10.9790/3021-0204719725openalex-snapshot
AN OVERVIEW ON CLUSTERING METHODS
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