Data mining is another term that is often confused with machine learning (ML). Here’s an easy explanation of the two terms, as well as the relationship between the two.
What is Machine Learning?
According to Stanford University, “Machine learning is the science of getting computers to act without being explicitly programmed.”
What is Data mining?
Data mining is the process of extracting implicit, previously unknown and potentially useful information from large data sets.
How does data mining relate to machine learning?
Data mining pulls from existing data to look for emerging patterns. For e.g., retailers can determine which items are the most popular for men and for women, which items are the most returned and which items are the most exchanged. This information can be used to help shape decision-making processes, such as which items should be featured more in marketing and which items or colours or sizes of specific items should be dropped from the inventory.
Machine learning takes it a step further, by actually learning from existing data. ML looks at patterns and is able to predict future behaviour by learning from the patterns. Data mining is often used as an information source on which machine learning is based. For e.g., machine learning would be able to identify the start of a new trend by identifying what the most popular items will be in future based on current and past trends.
How can data mining and machine learning be used in business?
There are a range of ways that your data can be analysed to provide you with decisions and predictions that will ensure improved performance, whether it’s determining which leads would be most likely to respond to your offer or which customers will likely roll on payments. If you’re interested in finding out more about the business applications of machine learning, read our blog or find out more about Genius, our Machine Learning as a Service offering that gives you the power of machine learning without the investment in infrastructure.