Industries from retail to finance are using clustering to personalize services, detect fraud, monitor equipment and improve ...

Nevertheless, unsupervised learning does have its uses: It can sometimes be good for reducing the dimensionality of a data set, exploring the pattern and structure of the data, finding groups of ...

Clustering is the most common process used to identify similar items in unsupervised learning. The task is performed with the goal of finding similarities in data points and grouping similar data ...

The Self-Organizing Feature Map (SOM) is an unsupervised learning ... data clustering. In classical computing, the SOM algorithm continuously adjusts weight vectors to reasonably group input ...

The first discussed business applications can benefit from supervised learning. This article will discuss unsupervised ... increase in data we have about individuals, groups, and even companies.

In unsupervised learning, the data has no labels. The machine just looks for whatever patterns it can find. This is like letting a dog smell tons of different objects and sorting them into groups ...

Brandon Freeman, founder of the Freeman Group ... uses an unsupervised learning approach to develop software that can train neural networks without the need for large-scale fleet data, simulation ...