Clustering

Clustering helps find hidden patterns in your data by grouping similar things for you, such as customer personas. There are two types of clustering techniques:

• Partitioned: Each and every item is clustered in one, and only one, cluster. One such method is K-means.
• Hierarchical: Creates the clusters by continually grouping or breaking items.

K-means clustering

How it works: How this algorithm operates is in its name. K is an input to the algorithm and refers to the number of buckets or clusters that will be created. Each item is assigned to a cluster based on the closest mean.

• Choosing the initial values is one of the toughest problems. Often, the algorithm might choose centroids as the initial k points.
• Then it clusters every point based on the closest mean.
• Then it evaluates if the initial values were the best central values.
• If not, then it reassigns the initial k points and repeats the process.

The details: Given that the algorithm is guessing the initial values, you can get different results for the same value of k.

Hierarchical clustering

There are two types of hierarchical clustering: agglomerative and divisive.

• Agglomerative clustering is bottom up, i.e., each item is its own cluster and then they are joined together to create larger clusters.
• Divisive clustering is top down, i.e., all items start in a single cluster and then are broken apart to create smaller clusters.

The details: Hierarchical clustering always produces the same results every time because the distance between items doesn’t change.