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This is part two of my series based on Lomit Patels Lean AI (OReilly, ISBN:978-1-492-05931-8). The first discussed business applications can benefit from supervised learning. This article will discuss unsupervised learning. Again, refer to the books Figure 5-1, included below, for an overview of the four key types of artificial intelligence (AI) leveraged in machine learning (ML).

Different types of machine learning and business applications

Most managers, both line and even IT, do not need to understand the intricacies of machine learning. However, a high level knowledge will help their organizations understand that AI is a tool and must be linked to real business problems. Having an idea of how the high level classifications of ML link to real world issues can help focus both the technical and business staff to provide effective solutions.

As a quick reminder, supervised learning is we understand the results we want identified. The features (Parameters, variables, whatever) we need can then be chosen and the data labeled appropriately. That allows analysis that examines data to see where they fit within known patters of results.

That is not always possible, nor preferable. Sometimes there are new relationship, things that might not be expected. In many business arenas, but especially in the case of consumer markets, there is so much data to wade through in order to identify a link before competitors recognize the same relationship thereby providing a critical competitive advantage. Unsupervised learning is ideal for exploring data with little or no knowledge about what it could represent. It can be very helpful in finding patterns in raw data when you may not know exactly what you are looking for, says Lomit Patel.

Let us look at a couple of examples.

Customer segmentation is a core marketing tool. The goal is to understand the different types of buyers, see what links groups of individuals as per traits, and then build marketing campaigns that accurately address the needs of each group, or cluster, of customers.

At first blush, that might seem to be something that could use supervised learning. After all, we know there are traits based on gender, age, income, and other segments that we can define, and into which customers can be classified. That type of segmentation is clearly amenable to supervised learning, and we shouldnt ignore any tool we have.

Whats changed is the exponential increase in data we have about individuals, groups, and even companies. So, for instance, it might end up that people who shop at store A are more likely to buy product X, regardless of their age. Analysis continues to find new ways of clustering people based on data ways we would have never thought of in advance and for which classification doesnt work.

That is the difference between classification and clustering, things that, at a high level, sound the same. Supervised learning is for when we know the classifications (cancer v no cancer), while unsupervised learning can cluster data points based on variables where no previous link might have been expected. Customer segmentation is becoming far more advanced with unsupervised learning.

This one is used ever day in ecommerce. Everyone has seen shopping, movie, and other sites that suggest people who like X also like Z. That is association. Supervised learning does not work, as we have no idea what people like until that like is expressed. By building a neural network that can analyze those likes, unsupervised training can lead to a system that learns from the data to make suggestions. That is much better than training a machine based on current preferences because, as every marketer knows, preferences are not constant.

That last phrase is critical. Cancer is cancer. We might find new cancers, or find out a specific new way to detect an existing one. At that point, algorithms can be updated, but were still specifying exactly what the machine should identify, using a fixed feature set.

Associations, relationships between products, preferences, and more, are often part of culture, and that culture is constantly undergoing change. A strong ML system is trained to look at all the data and notice relationships that are previously unknown, and even the loosening of previously strong relationships. It is unsupervised learning that allows the systems to not be limited by what we already think we know.

When you know the results you need to get, supervised learning is the way to go. However, with the modern volumes of data, organizations can gain new and unexpected insight from seemingly unrelated data points. Unsupervised learning is the tool that helps find those new relationships, the new patterns and links that add insight in many areas of business.

You might have noticed that not everything in the world is black and white. Well, supervised and unsupervised learning arent completely independent. While some of the discussion above hints at that, the next entry in this Management AI series will discuss just that why hybrid systems are useful.

More:

Management AI: Matching AI Models To Business Needs, Unsupervised Learning, Customer Segmentation, And Association - Forbes