Unsupervised Machine Learning Algorithms.
Table Of Contents:
- What Is Unsupervised Machine Learning?
- Examples Of Unsupervised Machine Learning.
- Types Of Unsupervised Learning Algorithms.
- Unsupervised Machine Learning Algorithms.
(1) What Is Unsupervised Machine Learning?
Unsupervised learning is a method in which a machine learns without supervision.
- The machine learns by itself in an Unsupervised Learning style.
- The training is provided to the machine with the data set that has not been labeled, classified, or categorized, and the algorithm needs to act on that data without supervision.
- Unsupervised learning aims to restructure the input data into new features or a group of objects with similar patterns.
(2) Examples Of Unsupervised Machine Learning.
Example-1: Organize computing clusters
- The geographic areas of servers is determined on the basis of clustering of web requests received from a specific area of the world.
- The local server will include only the data frequently created by people of that region.
Example-2: Social Network Analysis
- Social network analysis is conducted to make clusters of friends depending on the connection frequency between them.
- Such analysis reveals the links between the users of some social networking websites.
Example-3: Market Segmentation
Sales organizations can cluster or group their users into multiple segments on the basis of their prior billed items.
For instance, a big superstore can be required to send an SMS about grocery elements specifically to its users of groceries rather than sending that SMS to all its users.
It is not only is it cheaper but also superior; after all, it can be an irrelevant irritant to those who only buy clothing from the store.
The combining of users into multiple segments based on their buy history will provide the store to focus on the correct users for increasing sales and enhancing its profits.
(3) Types Of Unsupervised Machine Learning.
- Clustering
- Association
(4) Clustering Machine Learning.
- Clustering is a method of grouping objects into clusters such that the objects with the most similarities remain in a group and have less or no similarities with the objects of another group.
- Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.
(5) Association Machine Learning.
- An association rule is an unsupervised learning method that is used for finding the relationships between variables in a large database.
- It determines the set of items that occurs together in the dataset.
- The association rule makes marketing strategy more effective.
- Such as people who buy X items (suppose bread) also tend to purchase Y (Butter/Jam) items.
- A typical example of an Association rule is Market Basket Analysis.
(6) Unsupervised Machine Learning Algorithms.
- K-means clustering
- KNN (k-nearest neighbors)
- Hierarchal clustering
- Anomaly detection
- Neural Networks
- Principle Component Analysis
- Independent Component Analysis
- Apriori algorithm
- Singular value decomposition