Pros:

- Easy to implement
- Produces compact-shaped clusters

Cons:

- Must specify number of clusters in advance
- Sensitive to initial choices of centroids
- Not good at identifying clusters that donâ€™t follow a globular shape

- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (108)
- Regression (36)
- Classification (46)
- Logistic Regression (10)
- Support Vector Machine (10)
- Naive Bayes (4)
- Discriminant Analysis (5)
- Classification Evaluations (9)

- Classification & Regression Trees (CART) (23)

- Unsupervised Learning (46)
- Clustering (17)

- Regularization (6)
- Deep Learning (23)
- Data Preparation (43)
- General (5)
- Standardization (6)
- Missing data (7)
- Textual Data (16)
- Dimensionality Reduction (9)

- Categories: K-Means Clustering

Pros:

- Easy to implement
- Produces compact-shaped clusters

Cons:

- Must specify number of clusters in advance
- Sensitive to initial choices of centroids
- Not good at identifying clusters that donâ€™t follow a globular shape

Find out all the ways

that you can

- Other Questions in K-Means Clustering