Data mining and machine learning have been hot topics within the world of business for quite some time now. It allows companies to:
understand the needs of their customers much better,
perform a deep analysis of buying patterns,
gain a competitive advantage,
and much more.
Having this in mind, it is easy to see why data mining and machine learning combined have become so widely adopted over the years by companies in all types of industries.
Despite this, there is often some confusion surrounding these two forms of advanced data technology, with many believing that each term is interchangeable. However, while machine learning and data mining do share similarities, it is important to realize that they also differ in more ways than one. You might also want to learn more about data aggregation which is closely related to the topics discussed in this article.
So, what is the difference between data mining and machine learning? Several factors need to be taken into account, allowing us to differentiate between the two. Continue reading as we delve deeper into this topic.
Data mining is a process whereby available datasets are analysed in an effort to identify any patterns of anomalies that are unlikely to have been seen previously. Allowing organisations to make better business decisions, this form of technology essentially reviews data in order to determine which relationships are the most important and the ones which are the most frequent.
Throughout the process of data gathering and data mining, a company is able to utilise many techniques to extract information out of a large dataset. Along the way, this allows for the discovery of hidden and interesting patterns through:
association rule mining,
and many other techniques.
So, for businesses that need to continually utilize a great amount of data to make better business decisions, it can be incredibly useful to utilize data mining.
According to research, Alan Turing introduced the idea of a universal machine that could collect and process large amounts of data in his 1936 paper entitled On Computable Numbers. When you consider that modern day computers are built on the concepts pioneered by Turing, it’s clear that data mining has for many years served an important purpose, and this will no doubt continue to be the case for the foreseeable future.
In addition to this, data mining has evolved so that it is now able to identify a growing number of important trends that are hugely relevant to businesses nowadays. If each of these trends are taken into account, then it will become far easier for a company to gain an edge over their competitors. A failure to do so will simply result in customers choosing an alternative brand.
To further emphasise the influence of data mining, a recent study conducted by London South Bank University discovered that it can be used to develop customer-centric business intelligence for online retailers – something that is invaluable when it comes to developing appropriate marketing strategies and increasing profitability.
Ever since being coined as a term by Arthur Samuel on the creation of his checkers-playing program back in 1959, machine learning has been utilized a form of advanced technology by many businesses across the globe. More often than not, it is used to improve sales processes and increase efficiency, among several other uses.
A computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program.
Arthur Samuel (1959)
But when you consider the fact that it has made performing manual tasks increasingly simple over the years, it’s clear that machine learning will continue to play an important role in the future of companies across a wide range of industries in the years to come.
In many ways, machine learning works hand in hand with data mining, as it is often used as a way for data scientists to set the ball rolling for the machine learning process. Plus, just like data mining, machine learning is a form of technology that is rooted deep within data science. It’s for this very reason that many believe the two terms to be interchangeable. It’s understandable why this is the case too, as both machine learning and data mining utilise the same key algorithms.
But in actual fact, there are a few key factors that differentiate these two forms of technology.
One of the most prominent is that machine learning has the ability to mimic human behavior and become more efficient at performing tasks over time through a process of self-learning.
In addition to this, there’s also the fact that unlike data mining, machine learning takes the existing data and provides a basis for machines to teach themselves over time.
An example of machine learning in practice is a process such as fraud detection, a task that machines will more often than not be able to perform far more quickly than through human intervention alone. This is made possible thanks to the ability for machines to detect any suspicious activity at the earliest possible opportunity. In turn, this stops any potential fraudsters in their tracks before it’s too late.
What’s more, many services used today, such as search engines like Google, are an example of just how useful machine learning can be. After all, you now have the ability to enter search terms either through text or through voice search, after which you’ll be provided with associated searches that may be of great use to you when performing research online.
As you’ll now be aware, data mining and machine learning most definitely aren’t new inventions, as each form of technology came into practice long before the digital era. But while they have both been around for many years, their presence continues to be felt nowadays in more ways than one.
Overall, it would be appropriate to suggest that each form of technology has the potential to be incredibly useful for your business. So, now that you’re aware of the differences between the two, you’ll now have the knowledge you need to determine which one out of machine learning and data mining will benefit your business the most.
If you want to know more about the data gathering process or you are interested in starting your own web scraping project, check out our other blog posts for more information! There's also a solution to buy datasets from Oxylabs if your goal is to get ready-to-use public data to get insights for your business growth.
About the author
PR Team Lead
Adomas Sulcas is a PR Team Lead at Oxylabs. Having grown up in a tech-minded household, he quickly developed an interest in everything IT and Internet related. When he is not nerding out online or immersed in reading, you will find him on an adventure or coming up with wicked business ideas.
All information on Oxylabs Blog is provided on an "as is" basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website's terms of service or receive a scraping license.
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