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Benefits of Unsupervised Learning

Going beyond trained examples to learn

Most of the machine learning tasks centered around supervised learning. It works perfectly well for the pragmatic intelligent solution for most of the problem. But, sometime by the lack of training data set or the complexity of the problem may warrant us to devise unsupervised learning strategy. Datoin helps you to explore these options.

Use of Data Mining to solve Business Problems

Many traditional unsupervised learning techniques are used for data mining and exploration tasks. And, they were analysed by data scientist using workbench and tools. Datoin uses these techniques to solve certain business problem, help you to incorporate in your applications. Thus, eliminating the relevancy of data scientist on a daily basis.

Supervised Algorithm Enrichment

The clustering and topic modeling technique as part of data preprocessing and enrichment technique can improve the supervised algorithms accuracy. The datoin platform allows you to plug-in these techniques in any intelligent applications created using datoin platform.

Use cases

The use cases of Unsupervised Learning are many. Following are some of working examples of it.

News clustering

The news stories are published by hundreds of different publisher. If you are planning to build an aggregator which gathers news from web, then collating similar news is the way to go.

Anomaly Detection

Identification of items or events which do not conform to an expected pattern such as fraud detection, illegal access, sudden spike in activities etc.

Content Deduplication

Whether, it is plagiarism detection, merging two similar documents such as user contacts, grouping similar search results, the Datoin’s unsupervised learning will help you.

Customer Segmentation

By segmenting customer by their behaviour using transactions, browsing behaviour and social media, organisations can relate to customers in order to maximize the value of each customer to the business.

Product Development

Mining user surveys, customer reviews, etc is the way to go about iterative product development. It does not only help to uncover the unknown unknown issues, but also help to prioritize the issues which matters.

Content Organisation

Organisation of enterprise data or user generated content into automated tag generation to increase the discovery of content, easy access.



Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Although, in traditional sense cluster analysis is primarily used in exploratory data mining using workbench, it can be used in a way that can be used directly consumed by softwares.

Topic Modeling

Topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Although, topic models were first described and implemented in the context of natural language processing, they have applications in other fields.

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