Centroid Table

Attributes	Cluster_0	Cluster_1	Cluster_2	Cluster_3	Cluster_4
Balance	0.246	1.893	0.563	-0.467	1.396
Balance_Frequency	-0.042	0.300	0.435	-0.091	0.436
Purchases	-0.366	11.044	1.350	-0.098	-0.011
One-off Purchases	-0.247	10.439	1.150	-0.160	-0.224
Installment Purchases	-0.412	6.923	1.079	0.064	0.384
Cash Advance	0.360	0.408	-0.001	-0.398	0.228
Purchase Frequency	-0.906	1.033	1.117	0.644	0.008
One-off Purchases Frequency	-0.417	1.893	1.617	-0.041	-0.457
Purchases Installments Frequency	-0.792	0.969	0.899	0.584	0.211
Cash_Advance_Frequency	0.523	-0.269	-0.123	-0.529	-0.154
Cash_Advance_Trx	0.376	0.051	-0.039	-0.399	0.057
Purchases_Trx	-0.498	5.281	1.641	0.005	0.197
Credit_Limit	-0.048	3.019	1.009	-0.276	0.085
Payments	-0.040	8.040	0.837	-0.275	0.062
Minimum_Payments	-0.001	1.100	0.124	-0.164	11.210
Prc_Full_Payment	-0.425	1.077	0.298	0.370	-0.538
Tenure	-0.093	0.304	0.287	0.009	0.295

1- Which cluster in the Centroid table is mostly compacted and why?

2- Examine the clusters generated by RM, what customer segmentation does each of the cluster represent? Reassign meaningful names to the clusters (instead of generic names such as cluster_0, cluster_1, etc.) based on the characteristics of the segmentation (Offer your own analysis of what you think of clusters based on those numbers). Note that while there is no set way to name each cluster, you will need to provide justifications for the choices.