Clustering optimization in RFM analysis Based on k-Means
RFM stands for Recency, Frequency, and Monetary. RFM is a simple but effective method that can be applied to market segmentation. RFM analysis is used to analyze customer's behavior which consists of how recently the customers have purchased (recency), how often customer's purchases (frequ...
Saved in:
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2020-04-01.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 02565 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs20264_13628 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Gustriansyah, Rendra |e author |
| 100 | 1 | 0 | |a DRPM Kemenristekdikti of the Republic of Indonesia |e contributor |
| 700 | 1 | 0 | |a Suhandi, Nazori |e author |
| 700 | 1 | 0 | |a Antony, Fery |e author |
| 245 | 0 | 0 | |a Clustering optimization in RFM analysis Based on k-Means |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-04-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20264 | ||
| 520 | |a RFM stands for Recency, Frequency, and Monetary. RFM is a simple but effective method that can be applied to market segmentation. RFM analysis is used to analyze customer's behavior which consists of how recently the customers have purchased (recency), how often customer's purchases (frequency), and how much money customers spend (monetary). In this study, RFM analysis has been used for product segmentation is to be arrayed in terms of recent sales (R), frequent sales (F), and the total money spent (M) using the data mining method. This study has proposed a new procedure for RFM analysis (in product segmentation) using the k-Means method and eight indexes of validity to determine the optimal number of clusters namely Elbow Method, Silhouette Index, Calinski-Harabasz Index, Davies-Bouldin Index, Ratkowski Index, Hubert Index, Ball-Hall Index, and Krzanowski-Lai Index, which can improve the objectivity and similarity of data in product segmentation so that it can improve the accuracy of the stock management process. The evaluation results showed that the optimal number of clusters for the k-Means method applied in the RFM analysis consists of three clusters (segmentation) with a variance value of 0.19113. | ||
| 540 | |a Copyright (c) 2020 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science; Data Mining | ||
| 690 | |a clustering; k-Means; number of clusters; RFM; validity index | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 18, No 1: April 2020; 470-477 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v18.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20264/13628 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20264/13628 |z Get fulltext |