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Here we first learn about clustering.

**What is Clustering?**

How do I group these documents by topic?

How do I group my customers by purchase patterns?

Sort items into groups by similarity:

Items in a cluster are more similar to each other than they are to items in other clusters.

Need to detail the properties that characterize “similarity”

•Not a predictive method; finds similarities, relationships

Our Example: K-means Clustering

**What is Cluster Analysis?**

Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups.

**Types of Clusters: Well-Separated**

**Well-Separated Clusters: **

A cluster is a set of points such that any point in a cluster is closer (or more similar) to every other point in the cluster than to any point not in the cluster.

**Types of Clusters: Center-Based**

**Center-based**

A cluster is a set of objects such that an object in a cluster is closer (more similar) to the “center” of a cluster, than to the center of any other cluster

The center of a cluster is often a centroid, the average of all the points in the cluster, or a medoid, the most “representative” point of a cluster

**K-Means Clustering - What is it?**

Used for clustering numerical data, usually a set of measurements about objects of interest.

Input: numerical. There must be a distance metric defined over the variable space.

Euclidian distance

Output: The centers of each discovered cluster, and the assignment of each input to a cluster.

**Centroid**

**What Euclidian Distance?**

**K-means Clustering**

Characteristics

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