Analysis of DBSCAN and K-means algorithm for evaluating outlier on RFM model of customer behaviour

Siti Monalisa · Fitra Kurnia

23 citations0/0 files storedTELKOMNIKA (Telecommunication Computing Electronics and Control)

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

The aim of study is to discover outlier of customer data to found customer behaviour. The customer behaviour determined with RFM (Recency, Frequency and Monetary) models with K-Mean and DBSCAN algorithm as clustering customer data. There are six step in this study. The first step is determining the best number of clusters with the dunn index (DN) validation method for each algorithm. Based on the dunn index, the best cluster values were 2 clusters with DN value for DBSCAN 1.19 which were minpts and epsilon value 0.2 and 3 and DN for K-Means was 1.31. The next step was to cluster the dataset with the DBSCAN and K-Means algorithm based on the best cluster that was 2. DBSCAN algorithm had 37 outliers data and K-means algorithm had 63 outliers (cluster 1 are 26 outliers and cluster 2 are 37 outliers). This research shown that outlier in DBSCAN and K-Means in cluster 1 have similarities is 100%. But overal outliers similarities is 67%. Based the outliers shown that the behaviour of customers is a small frequency of spending but high recency and monetary.

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 — 23 citations exist upstream.

References

2009Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty 2011Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study 2014A Two-Phase Clustering Analysis for B2B Customer Segmentation 2016The Research and Application of Customer Segmentation on E-Commerce Websites 2011Customer portfolio analysis using the SOM

Cited by

2021Who Donates on Line? Segmentation Analysis and Marketing Strategies Based on Machine Learning for Online Charitable Donations in Taiwan 2023Customer segmentation with RFM models and demographic variable using DBSCAN algorithm 2019Spatial Clustering Based on Analysis of Big Data in Digital Marketing

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.