# Distance Measures

### What are some common distance metrics that can be used in clustering?

Some common distance metrics are: Euclidean distance, Mahalanobis distance, Manhattan distance, Minkowski distance, Cosine Similarity, Jaccard distance

### What is Euclidean Distance?

The Euclidean Distance, or L2 norm, is the most common distance metric used in clustering.

### What is Cosine Similarity?

Cosine Similarity measures similarity using the cosine of the angle generated between two vectors in p-dimensional space.

### What is KL Divergence?

The Kullback-Leibler (KL) Divergence is a method of quantifying the similarity between two statistical distributions.

### What is Jaccard Index / Distance?

The Jaccard Index measures similarity for two sets of data by computing the ratio of items present in both sets

### What is Mutual Information (MI)?

The concept of Mutual Information measures the amount of information shared between two random variables

### What is Minkowski Distance?

The Minkowski Distance is a general form for computing distances using an Lp norm.

### What is Manhattan Distance?

The Manhattan distance, or L1 norm, measures the sum of absolute distance between two vectors.