Example Input
[
{
"id": 1,
"age": 25,
"income": 50000,
"spending": 35000,
"savings": 15000,
"satisfaction": 4.2
},
{
"id": 2,
"age": 35,
"income": 75000,
"spending": 45000,
"savings": 30000,
"satisfaction": 4.5
},
{
"id": 3,
"age": 45,
"income": 100000,
"spending": 60000,
"savings": 40000,
"satisfaction": 4.8
}
]Clustering Methods:
- K-Means: Partitions data into k clusters by minimizing within-cluster variance
- Hierarchical: Builds clusters by progressively merging similar points
- Silhouette Score ranges from -1 to 1, with higher values indicating better clustering