Unsupervised machine learning involves training models using data that consists only of feature values without any known labels. Unsupervised machine learning algorithms determine relationships between the features of the observations in the training data.
The goal of most of these techniques is to cluster the data-points such that the data-points of the same ground truth class are assigned the same cluster.
The deep learning-based clustering techniques are different from traditional clustering techniques as they cluster the data points by finding complex patterns rather than using simple pre-defined metrics like intra-cluster euclidean distance. [Link].
Methods:
Hierarchical Clustering.
K-Means Clustering.
DBSCAN.
HDBSCAN.
Fuzzzy C-Means.
Mean Shift.
Agglomerative.
OPTICS.
Note: Non-parametric deep clustering methods which utilize deep clustering when the number of clusters is not known apriorly and needs to be inferred.
Training a Clustering Model
One of the most commonly used algorithms is K-Means clustering, which consists of the following steps:
The feature (x) values are vectorized to define n-dimensional coordinates (where n is the number of features). In the flower example, we have two features: number of leaves (x1) and number of petals (x2). So, the feature vector has two coordinates that we can use to conceptually plot the data points in two-dimensional space ([x1,x2])
You decide how many clusters you want to use to group the flowers - call this value k. For example, to create three clusters, you would use a k value of 3. Then k points are plotted at random coordinates. These points become the center points for each cluster, so they're called centroids.
Each data point (in this case a flower) is assigned to its nearest centroid.
Each centroid is moved to the center of the data points assigned to it based on the mean distance between the points.
After the centroid is moved, the data points may now be closer to a different centroid, so the data points are reassigned to clusters based on the new closest centroid.
The centroid movement and cluster reallocation steps are repeated until the clusters become stable or a predetermined maximum number of iterations is reached.
Evaluating a Clustering Model
Since there's no known label with which to compare the predicted cluster assignments, evaluation of a clustering model is based on how well the resulting clusters are separated from one another.
There are multiple metrics that you can use to evaluate cluster separation, including:
Average distance to cluster center: How close, on average, each point in the cluster is to the centroid of the cluster.
Average distance to other center: How close, on average, each point in the cluster is to the centroid of all other clusters.
Maximum distance to cluster center: The furthest distance between a point in the cluster and its centroid.
Silhouette: A value between -1 and 1 that summarizes the ratio of distance between points in the same cluster and points in different clusters (The closer to 1, the better the cluster separation).
Apriori Algorithm.
ECLAT Algorithm.
PP Trees.
Principal Component Analysis (PCA).
Random Projection.
NMF.
T-SNE.
UMAP.
