While data mining and machine learning share certain characteristics, these two terms don’t mean the same thing. Both fall under the general category of data science, which uses scientific methods, systems, processes, and algorithms to extract knowledge from data.
But there are some important differences between the two.
The Difference Between Data Mining and Machine Learning
Data mining provides techniques for data management, while machine learning offers methods for data analysis. Both are engaged in the knowledge discovery process and are extracting information from data. But companies can use data mining tools that don’t involve machine learning and vice versa.
Data mining, as the name suggests, refers to the process of extracting actionable information from a large data set. Companies use data mining to look for patterns in data and then apply those patterns to make decisions or predictions. There are typically some manual aspects of data mining, and it is designed for use by people. It relies on vast stores of data and big data services.
Machine learning describes the design and development of algorithms that computers use to learn without the assistance of humans. Machine learning is automated and does not require human intervention. While it is sometimes used as a means of conducting useful data mining, human contact is limited to setting up the algorithms.
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Frequently Asked Questions
Is data mining important for machine learning?
Machine learning is a technique for data mining, but it also has other data analysis applications. So, data mining can use machine learning, but machine learning does more than just data mining.
How is AI used in data mining?
Artificial intelligence (AI) is used in data mining to automate tasks commonly performed by humans. Unlike machine learning, AI doesn’t depend on algorithms that enable learning. Instead, it uses programmed control systems to determine solutions on its own by making calculations.
What are the three elements of data mining?
The three elements of data mining are…
- Extract, transform, and load transaction data onto a data storage system
- Manage the data in a multidimensional database system
- Provide data access to business analysts.
