This master thesis investigates the utilisation of various graph based machine learning models for solving a customer segmentation problem, a task coupled to Customer Relationship Management, where the objective is to divide customers into different groups based on similar attributes. More specifically a customer segmentation problem is solved via an unsupervised machine learning technique named clustering, using the k-means clustering algorithm. Three different representations of customers as a vector of attributes are created and then utilised by the k-means algorithm to divide users into different clusters. The first representation is using a elementary feature vector and the other two approaches are using feature vectors produced by graph based machine learning models. Results show that similar grouping are found but that results vary depending on what data is included in the instantiation and training of the various approaches and their corresponding models.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-81892 |
| Date | January 2020 |
| Creators | Delissen, Johan |
| Publisher | Luleå tekniska universitet, Institutionen för system- och rymdteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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