Data mining and machine learning techniques are divided into two main classes:

• The directed, or supervised, approach: You use known examples and apply information to unknown examples to predict selected target variable(s)
• The undirected, or unsupervised approach: You discover new patterns inside the dataset as a whole

The most common undirected techniques are clustering, dimensionality reduction, and affinity grouping, also known as basket analysis or association rules. An example of clustering is looking through a large number of initially undifferentiated customers and trying to see if they fall into natural groupings based on similarities or dissimilarities in their features. This is a pure example of undirected data mining where the user has no preordained agenda and hopes that the data mining tool will reveal some meaningful structure. Affinity grouping is a special kind of clustering that identifies events or transactions that occur simultaneously. A well-known example of affinity grouping is market basket analysis, which attempts to understand what items are sold together at the same time.

In this section, you will learn about four very popular undirected methods:

• Principal Components Analysis 
• Exploratory Factor Analysis 
• Hierarchical clustering
• K-means clustering