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What are the examples of Unsupervised Learning?
Unsupervised learning is when it can provide a set of unlabelled data, which it is required to analyze and find patterns inside. The examples are dimension reduction and clustering. The training is supported to the machine with the group of data that has not been labeled, classified, or categorized, and the algorithm required to facilitate on that data without some supervision. The objective of unsupervised learning is to restructure the input record into new features or a set of objects with same patterns.
Cluster analysis is used to form groups or clusters of the same records depending on various measures made on these records. The key design is to define the clusters in ways that can be useful for the objective of the analysis. This data has been used in several areas, such as astronomy, archaeology, medicine, chemistry, education, psychology, linguistics, and sociology.
Google is an instances of clustering that needs unsupervised learning to group news items depends on their contents. Google has a set of millions of news items written on multiple topics and their clustering algorithm necessarily groups these news items into a small number that are same or associated to each other by using multiple attributes, including word frequency, sentence length, page count, etc
There are various examples of Unsupervised Learning which are as follows −
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.
Social network analysis − Social network analysis is conducted to make clusters of friends depends on the frequency of connection between them. Such analysis reveals the links between the users of some social networking website.
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 required to send an SMS about grocery elements specifically to its users of grocery 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 the correct users for increasing sales and enhancing its profits.
Astronomical data analysis − Astronomers need high telescopes to study galaxies and stars. The design in light or combining of lights received from multiple parts of the sky help to recognize multiple galaxies, planets, and satellites.
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