Unsupervised learning problems further grouped into clustering and association problems.
Clustering is an important concept when it comes to unsupervised learning. It mainly deals with finding a structure or pattern in a collection of uncategorized data. Clustering algorithms will process your data and find natural clusters(groups) if they exist in the data. You can also modify how many clusters your algorithms should identify. It allows you to adjust the granularity of these groups.
There are different types of clustering you can utilize:
In this clustering method, Data are grouped in such a way that one data can belong to one cluster only.
In this clustering technique, every data is a cluster. The iterative unions between the two nearest clusters reduce the number of clusters.
Example: Hierarchical clustering
In this technique, fuzzy sets is used to cluster data. Each point may belong to two or more clusters with separate degrees of membership.
Here, data will be associated with an appropriate membership value. Example: Fuzzy C-Means
This technique uses probability distribution to create the clusters
Example: Following keywords
- “man’s shoe.”
- “women’s shoe.”
- “women’s glove.”
- “man’s glove.”
can be clustered into two categories “shoe” and “glove” or “man” and “women.”