Today’s fast-paced digital world has popularized so many new terms and phrases that we are often overwhelmed with understanding these terms. And people who coin these new and unfamiliar words interchangeably need to realize that they mean two different things.
Specifically, that’s the problem facing data mining and machine learning. The two terms’ definitions are sometimes blurred because they have the same characteristics.
To clearly understand the two terms, here’s an explanation of the differences between data mining and machine learning.
Data Mining and Machine Learning: Definition
Data mining is considered a process of extracting useful information from large amounts of data. Data mining is used to find new, accurate, and functional patterns in data, looking for meaning and relevant information for organizations or individuals who need it. Data mining is one of the tools used by humans today.
On the other hand, machine learning is discovering algorithms that have automatically enhanced the experience and capabilities of a system derived from data.
Machine learning is the design, study, and development of algorithms that enable machines to learn without human intervention.
Machine learning is a tool for making machines more intelligent, removing the human element (but not eliminating the humans themselves; that would be wrong).
Data Mining and Machine Learning Differences
The similarities between the two terms are slight. However, the two terms still need to be clarified for many people because of the overlapping data. On the other hand, the two terms have several significant differences. Let’s see how the two terms differ.
Here’s how the difference between data mining and machine learning is:
History
For starters, data mining predates machine learning by two decades, initially called knowledge discovery in database (KDD). Data mining is still referred to as KDD in some areas.
Machine learning made its debut in the checkers game program. Data mining has been around since the 1930s, while machine learning emerged in the 1950s.
Purpose
Data mining is designed to extract rules from large amounts of data. At the same time, machine learning teaches computers how to learn and understand given parameters.
In other words, data mining is a research method to determine specific results based on the total data collected. On the other hand, we have machine learning which is used to train systems to perform complex tasks and use collected data and experience to become more innovative.
Usage
Data mining relies on vast stores of data (e.g., Big Data), which are then, in turn, used to make forecasts for businesses and other organizations. Machine learning, on the other hand, works with algorithms, not raw data.
Human Factors
Here are significant differences. Data mining relies on human intervention and is ultimately made for use by people.
Meanwhile, machine learning was created because it can teach itself and does not depend on human influence or action. With someone using and interacting with it, data mining can work.
On the other hand, human contact with machine learning is entirely limited by the initial algorithm setup.
Then let it work, sort of a “set it and forget it” process. People intervene in data mining and systems care for themselves with machine learning.
Relations with One Another
Data mining is a process that combines two elements: databases and machine learning. The former provides data management techniques, while the latter supplies data analysis techniques.
So while data mining requires machine learning, machine learning does not necessarily require data mining. Although, there are cases where information from data mining is used to see the relationship between relationships.
After all, it’s easier to make comparisons if you have at least two pieces of information to compare with each other.
As a result, the information collected and processed through data mining can then be used to assist machine learning, but it is not necessary. Think of it more as a handy convenience to have.
Ability to Grow
Here’s something easy to compare between the two terms: data mining cannot learn or adapt, which is the point with machine learning.
Data mining follows predefined and static rules, while machine learning adapts algorithms as the right circumstances manifest themselves.
Data mining is only as bright as the user input parameters, whereas machine learning means computers are getting smarter
Application
In terms of utility, each process has a specialization that must be carved out. Data mining is employed in retail to understand customers’ buying habits, helping businesses formulate more successful sales strategies.
Social media is like a playground for data mining because collecting information from user profiles, questions, keywords, and shares can be brought together.
This process will help advertisers build relevant promotions. The financial world uses data mining to research potential investment opportunities and the likelihood of startup success.
Gathering such information helps investors decide whether they want to commit money to new projects. If data mining had been perfected back in the mid-90s, it could prevent the collapse of the Internet startups that took off in the late 90s.
Meanwhile, companies are using machine learning for purposes such as self-driving cars, credit card fraud detection, online customer service, intercepting e-mail spam, business intelligence (e.g., managing transactions, gathering sales results, selecting business initiatives), and personalizing marketing.
Companies that rely on machine learning include high-profile companies like Yelp, Twitter, Facebook, Pinterest, Salesforce, and a few of the search engines you may have heard of: Google.
Is There Any Similarities Between Data Mining and Machine Learning?
Data mining and machine learning are both part of the field of Data Science, as they both involve using data to solve complex problems.
Due to this overlap, the terms are often used interchangeably, even though they have distinct differences. Machine learning can be used as a means of conducting data mining, and data gathered from data mining can be used to train machines.
Both processes utilize similar algorithms for identifying patterns in data, but their ultimate goals differ.
Closing
As more tasks and problems are being handled through digital solutions, both data mining and machine learning continue to grow in importance.
The prevalence of big data means that data mining will always be in demand, while the increasing use of smart machines and artificial intelligence drives the need for machine learning.
Although it is difficult to predict which offers the most potential, machine learning may have more opportunities due to the growing interest in AI and smart devices.
However, this does not mean that data mining is a less valuable skill, as the amount of data in the digital universe is projected to grow significantly in the coming years.