With clustering methods, we enter the category of unsupervised machine learning because their purpose is to group observations that have similar characteristics. Clustering methods do not use the output information for the training purpose but leave the algorithm in order to set the output. In the clustering procedure, we can only use visualizations to control the quality of final solution.

K-Means is the most popular classification method, where “K” represents the number of clusters the user chooses to create. (Note that there are a plethora of techniques for choosing the K value, such as the elbow method.)

Basically, what K-Means does with data points:

Randomly chooses K centers in the data.

Recalculate the center of each cluster.

Assign each data point to the nearest randomly created centers.

If the centers do not change, the process is over. Otherwise, we return to step 2. (In order to avoid ending in an infinite loop if the centers continue to change, set a maximum number of iterations in advance.)

The below-mentioned graphic applies K-Means to a building dataset. Each column of this graph shows the efficiency of each building. The 4 measures concern air conditioning, connected equipment (refrigerators, microwaves, etc.), heating gas and domestic gas. We chose K = 2 for grouping, which makes it easy to interpret one cluster as a group of various efficient buildings and the other group as inefficient buildings. On the left, you can see the location of the buildings and on the right, you see two of the four dimensions we used as inputs: the heating gas and the connected equipment.

In exploring clustering, you will encounter some very useful algorithms, such as noise-based application density-based spatial clustering (DBSCAN), cluster hierarchical clustering, medium-shift clustering, expectation-maximization clustering with the use of Gaussian mixing patterns, among others.