### What are some pros and cons of K-Means Clustering?

Pros: Easy to implement

Cons: Must specify number of clusters in advance

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Pros: Easy to implement

Cons: Must specify number of clusters in advance

Being that clustering is a distance-based algorithm, outliers can have multiple undesired effects on the quality of the clusters produced.

K-Means ++ has been generally shown to be the best initialization approach to use when performing K-Means clustering.

Using an objective function that minimizes the within-cluster sum of squares (WCSS) causes K-Means to produce spherically shaped clusters.

K-Means minimizes the total within-cluster sum of squares (WCSS)

The final cluster assignments of the K-Means algorithm can be sensitive to the location of the initial centroids.

The most common way to choose k is to run the algorithm over a range of values

K-Means starts by selecting initial centroids for the k-clusters by randomly choosing k observations

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- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (114)
- Regression (42)
- 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 (55)
- Clustering (28)
- Distance Measures (9)
- Dimensionality Reduction (9)

- Deep Learning (23)
- Data Preparation (34)
- General (5)
- Standardization (6)
- Missing data (7)
- Textual Data (16)