Some technologies are so interrelated that it can be difficult to understand their differences fully. Some of these concepts that are intertwined are Machine Learning (ML) and Data Mining (DM).
Nowadays, these technologies help companies to develop tools and solutions that can “make decisions” and even take measures based on the behavior of consumers.
You have to think about the DM, in a simple way, as a search for information. It could be people, concepts, behaviors or devices that professionals use personally or commercially.
Usually, the objective of using Data Mining is to discover some preliminary knowledge in an area where there was really little knowledge in advance or be able to predict future observations accurately.
The main difference between DM and ML is that, without human participation in Data Mining, it cannot work, but in Machine Learning human effort is involved only when the algorithm is defined.
Also, the procedures in Data Mining can be “unsupervised” (we do not know the response-discovery) or “supervised” (we know the answer-prediction). Common Data Mining techniques would include cluster analysis, classification and regression trees, and neural networks.
Machine Learning and Data Mining use the same key algorithms to discover patterns in the data. However, their process and, consequently, their usefulness, are different.
Unlike Data Mining, in the ML, the machine must automatically learn the parameters of the models from the data. Machine Learning uses self-learning algorithms to improve its performance in an experienced task over time. It can be used to reveal ideas and provide comments almost in real time.
It is necessary for the professional who specializes in the data to keep in mind the differences since the amount of data will only increase. By the year 2020, our universe of accumulated digital data will grow from 4.4 zettabytes to 44 zettabytes.
As we accumulate more data, the demand for advanced technical skills from Data Mining and Machine Learning will force the industry to evolve to keep up.
We are likely to see more significant overlap between the two technologies because they intersect to improve the collection and ease of use of large amounts of data for analytical purposes.