K-Means with their feature selection in RapidMiner
4 min readAug 31, 2021
Related Research
Design
Without Feature Selection
- accuracy: 65.52%
- split data: 0.01, accuracy: 66.22%
Unsupervised Feature Selection with k-means
- balance of simplicity: 1.0 = accuracy: 63.23%
- balance of simplicity: 0.5 = accuracy: 63.23%
- split data: 0.01, balance of simplicity: 1.0 = accuracy: 57.72%
- split data: 0.01, balance of simplicity: 0.5 = accuracy: 66.22%
Try to use different feature selection model
K-Means (H2O)
- notsplit 0.5
- notsplit 1.0
- split 0.5
- split 1.0
X-Means
- notsplit 0.5
- notsplit 1.0
- split 0.5
- split 1.0
K-NN
- split max 20
- split max 30
Naive Bayes
- split max 20
- split max 30
Decission Tree
- split max 20
- split max 30
