Data Mining vs. Machine Learning: What’s The Difference?

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Data Mining vs. Machine Learning: What’s The Difference?

  • 23/01/2020

Data mining isn’t a new invention that came with the digital age. The concept has been around for over a century but came into greater public focus in the 1930s.

Data Mining vs. Machine Learning

According to
Hacker Bits, one of the first
modern moments of data mining
occurred in 1936, when Alan Turing
introduced the idea of a universal machine that could perform computations
similar to those of modern-day computers.

Forbes also
reported on Turing’s development of the “Turing
in 1950 to determine if a computer has real intelligence or
not. To pass his test, a computer needed to fool a human into believing it was
also human. Just two years later, Arthur Samuel created The Samuel
Checkers-playing Program that appears to be the world’s first self-learning
program. It miraculously learned as it played and got better at winning by
studying the best moves.

We’ve come a
long way since then. Businesses are now harnessing data mining and machine
learning to improve everything from their sales processes to interpreting financials
for investment purposes. As a result, data scientists have become vital
employees at organizations all over the world as companies seek to achieve
bigger goals with data science than ever before.

Data Mining vs. Machine Learning vs. Data Science

With big
data becoming so prevalent in the business world, a lot of data terms tend to
be thrown around, with many not quite understanding what they mean. What
is data mining?
Is there a difference between machine learning vs.
data science? How do they connect to each other? Isn’t machine learning just
artificial intelligence? All of these are good questions, and discovering their
answers can provide a deeper, more rewarding understanding of data science and
analytics and how they can benefit a company.

Both data
mining and machine learning are rooted in data science and generally fall under
that umbrella. They often intersect or are confused with each other, but there
are a few key distinctions between the two. Here’s a look at some data mining
and machine learning differences between data mining and machine learning and
how they can be used.

Data Use

One key difference between machine learning and data mining is how they are used and applied in our everyday lives. For example, data mining is often used by machine learning to see the connections between relationships. Uber uses machine learning to calculate ETAs for rides or meal delivery times for UberEATS.

Data mining can be used for a variety of purposes, including financial research. Investors might use data mining and web scraping to look at a start-up’s financials and help determine if they want to offer to fund. A company may also use data mining to help collect data on sales trends to better inform everything from marketing to inventory needs, as well as to secure new leads. Data mining can be used to comb through social media profiles, websites, and digital assets to compile information on a company’s ideal leads to start an outreach campaign. Using data mining can lead to 10,000 leads in 10 minutes. With this much information, a data scientist can even predict future trends that will help a company prepare well for what customers may want in the months and years to come.

learning embodies the principles of data mining, but can also make automatic
correlations and learn from them to apply to new algorithms. It’s the
technology behind self-driving cars that can quickly adjust to new conditions
while driving. Machine learning also provides instant recommendations when a
buyer purchases a product from Amazon. These algorithms and analytics are
constantly meant to be improving, so the result will only get more accurate
over time. Machine learning isn’t artificial intelligence, but the ability to
learn and improve is still an impressive feat.

Banks are already using and investing in machine learning to help look for fraud when credit cards are swiped by a vendor. CitiBank invested in global data science enterprise Feedzai to identify and eradicate financial fraud in real-time across online and in-person banking transactions. The technology helps to rapidly identify fraud and can help retailers protect their financial activity.

Pattern Recognition

Collecting data is only part of the challenge; the other part is making sense of it all. The right software and tools are needed to be able to analyze and interpret the huge amounts of information data scientists collect and find recognizable patterns to act upon. Otherwise, the data would largely be unusable unless data scientists could devote their time to looking for these complex, often subtle, and seemingly random patterns on their own. And anyone even somewhat familiar with data science and data analytics knows this would be an arduous, time-consuming task.

could use data to shape their sales forecasting or determine what types of
products their customers really want to buy. For example, Walmart collects
point of sales from over 3,000 stores for its data warehouse. Vendors can see
this information and use it to identify buying patterns and guide their inventory
predictions and processes for the future.

It’s true that data mining can reveal some patterns through classifications and sequence analysis. However, machine learning takes this concept a step further by using the same algorithms data mining uses to automatically learn from and adapt to the collected data. As malware becomes an increasingly pervasive problem, machine learning can look for patterns in how data in systems or the cloud is accessed. Machine learning also looks at patterns to help identify which files are actually malware, with a high level of accuracy. All this is done without the need for constant monitoring by a human. If abnormal patterns are detected, an alert can be sent out so action can be taken to prevent the malware from spreading.

Improved Accuracy

Both data mining and machine learning can help improve the accuracy of the data collected. However, data mining and how it’s analyzed generally pertains to how the data is organized and collected. Data mining may include using extracting and scraping software to pull from thousands of resources and sift through data that researchers, data scientists, investors, and businesses use to look for patterns and relationships that help improve their bottom line.

One of the primary foundations of machine learning is data mining. Data mining can be used to extract more accurate data. This ultimately helps refine your machine learning to achieve better results. A person may miss the multiple connections and relationships between data, while machine learning technology can pinpoint all of these moving pieces to draw a highly accurate conclusion to help shape a machine’s behavior.

Machine learning can enhance relationship intelligence in CRM systems to help sales teams better understand their customers and make a connection with them. Combined with machine learning, a company’s CRM can analyze past actions that lead to a conversion or customer satisfaction feedback. It can also be used to learn how to predict which products and services will sell the best and how to shape marketing messages to those customers.

The Future of Data Mining and Machine Learning

The future
is bright for data science as the amount of data will only increase. By 2020,
our accumulated digital universe of data will grow from 4.4
zettabytes to 44 zettabytes
, as reported by Forbes. We’ll also
create 1.7 megabytes of new information every second for every human being on
the planet.

As we amass
more data, the demand for advanced data mining and machine learning techniques
will force the industry to evolve in order to keep up. We’ll likely see more
overlap between data mining and machine learning as the two intersect to
enhance the collection and usability of large amounts of data for analytics

According to reporting from Bio-IT World, the future of data mining points to predictive analysis, as we’ll see advanced analytics across industries like medical research. Scientists will be able to use predictive analysis to look at factors associated with disease and predict which treatment will work the best.

We’re just
scratching the surface of what machine learning can do and how it will spread
to help scale our analytical abilities and improve our technology. According to
reporting from Geekwire, as our billions of machines become connected,
everything from hospitals to factories to highways can be improved with IoT
technology that can learn from other machines.

But some experts have a different idea about data mining and machine learning altogether. Instead of focusing on their differences, you could argue that they both concern themselves with the same question: “How we can learn from data?” At the end of the day, how we acquire and learn from data is really the foundation for emerging technology. It’s an exciting time not just for data scientists but for everyone that uses data in some form.

To find out more about Data Mining, check out this article discussing the difference between Data Mining and Data Harvesting.

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