Machine Learning And Data Mining
Data Mining Vs. Machine Learning: The Key Difference
Our rapidly growing digital world has popularized so many new terms and phrases that it’s easy to get overwhelmed or lose track. The onslaught of technobabble is overwhelming. And people are liable to use strange new words interchangeably, unaware that the words mean two different things.
Specifically, that’s the issue facing “data mining” and “machine learning. ” The line between the two terms sometimes gets blurred due to some shared characteristics. In this article we will cover the following topics that will give you a clear understanding of the difference between data mining and machine learning:
What is data mining?
What is machine learning?
Similarities between data mining and machine learning
Differences between data mining and machine learning
Enhance your AI skill set and give a boost to your career with the Artificial Intelligence Course.
What is Data Mining?
Data mining is considered the process of extracting useful information from a vast amount of data. It’s used to discover new, accurate, and useful patterns in the data, looking for meaning and relevant information for the organization or individual who needs it. It’s a tool used by humans.
What is Machine Learning?
On the other hand, machine learning is the process of discovering algorithms that have improved courtesy of experience derived from data. It’s the design, study, and development of algorithms that permit machines to learn without human intervention. It’s a tool to make machines smarter, eliminating the human element (but not eliminating humans themselves; that would be wrong).
What Do They Have in Common?
Both data mining and machine learning fall under the aegis of Data Science, which makes sense since they both use data. Both processes are used for solving complex problems, so consequently, many people (erroneously) use the two terms interchangeably. This isn’t so surprising, considering that machine learning is sometimes used as a means of conducting useful data mining. While data gathered from data mining can be used to teach machines, the lines between the two concepts become a bit blurred.
Furthermore, both processes employ the same critical algorithms for discovering data patterns. Although their desired results ultimately differ, something which will become clear as you read on.
Difference between Data Mining and Machine Learning
So we see that their similarities are few, but it’s still natural to confuse the two terms because of the overlap of data. On the other hand, there’s a considerable number of differences between the two. So for the sake of clarity and organization, we are going to give each one its bullet item.
Let’s dig in to find out some of the differences between data mining and machine learning:
Their Age
For starters, data mining predates machine learning by two decades, with the latter initially called knowledge discovery in databases (KDD). Data mining is still referred to as KDD in some areas. Machine learning made its debut in a checker-playing program. Data mining has been around since the 1930s; machine learning appears in the 1950s.
Their Purpose
Data mining is designed to extract the rules from large quantities of data, while machine learning teaches a computer how to learn and comprehend the given parameters. Or to put it another way, data mining is simply a method of researching to determine a particular outcome based on the total of the gathered data. On the other side of the coin, we have machine learning, which trains a system to perform complex tasks and uses harvested data and experience to become smarter.
What They Use
Data mining relies on vast stores of data (e. g., Big Data), which then, in turn, is used to make forecasts for businesses and other organizations. Machine learning, on the other hand, works with algorithms, not raw data.
The Human Factor
Here’s a rather significant difference. Data mining relies on human intervention and is ultimately created for use by people. Whereas machine learning’s whole reason for existing is that it can teach itself and not depend on human influence or actions. Without a flesh and blood person using and interacting with it, data mining flat out cannot work. Human contact with machine learning, on the other hand, is pretty much limited to setting up the initial algorithms. And then just letting it be, a sort of “set it and forget it” process. People babysit data mining; the systems take care of themselves with machine learning.
How They Relate to Each Other
Also, data mining is a process that incorporates two elements: the database and machine learning. The former provides data management techniques, while the latter supplies data analysis techniques. So while data mining needs machine learning, machine learning doesn’t necessarily need data mining. Though, there are cases where information from data mining is used to see connections between relationships. After all, it’s hard to make comparisons unless you have at least two pieces of information that compare against each other! Consequently, information gathered and processed via data mining can then be used to help a machine learn, but again, it’s not a necessity. Think of it more as a convenience that’s handy to have.
The Ability to Grow
Here’s an easy one: data mining can’t learn or adapt, whereas that’s the whole point with machine learning. Data mining follows pre-set rules and is static, while machine learning adjusts the algorithms as the right circumstances manifest themselves. Data mining is only as smart as the users who enter the parameters; machine learning means those computers are getting smarter.
How They Are Used
In terms of utility, each process has its specialty carved out. Data mining is employed in the retail industry to fathom their customers’ buying habits, thereby helping businesses formulate more successful sales strategies. Social media is a fertile playground for data mining, as gathering information from user profiles, queries, keywords, and shares can be brought together. It will help advertisers put together relevant promotions. The world of finance uses data mining for researching potential investment opportunities and even the likelihood of a startup’s success. Gathering such information helps investors decide if they want to commit money to new projects. If data mining was perfected back in the mid-90s, it could very well have prevented the excellent Internet startup collapse of the late 90s.
Meanwhile, companies use machine learning for purposes like self-driving cars, credit card fraud detection, online customer service, e-mail spam interception, business intelligence (e. g., managing transactions, gathering sales results, business initiative selection), and personalized marketing. Companies that rely on machine learning include heavy hitters such as Yelp, Twitter, Facebook, Pinterest, Salesforce, and a little search engine you may have possibly heard of: Google.
Accelerate your career with the AI and Machine Learning Certification Courses with Purdue University collaborated with IBM.
So What Does This All Mean?
Every day, a little more of our world turns to digital solutions to handle tasks and solve problems. It’s a big enough digital world out there’s more than sufficient room for both data mining and machine learning to thrive. The continued dominance of Big Data means that there will always be a need for data mining. And the continued drive and demand for smart machines will ensure that machine learning remains a very much in-demand skill.
Which offers the most potential, you may wonder? There is no clear cut answer, but we can make a decent, informed guess. The increased interest in artificial intelligence and smart devices and the continued rise in the use of mobile devices are good signs. Between the two processes, machine learning may offer the best opportunities.
That doesn’t mean that data mining is, by any means, a dead-end career. According to Forbes, the total accumulated data in our digital universe will grow from 2019’s total of 4. 4 zettabytes to approximately 44 zettabytes or 44 trillion gigabytes of data. Yes, notice the missing decimal point between those two values!
Want to Get in on Machine Learning?
If you’re looking for an excellent career choice, you can’t miss a job in the field of machine learning. The demand (and salaries! ) for machine learning engineers is on the rise. The average salary of a Machine Learning Engineer is around $146K, with a growth rate last year of 344p percent!
If you want to become a part of this exciting, dynamic world, then Simplilearn has the tools to get you started on your way. The Machine Learning Certification Course will make you an expert in machine learning. You will master machine learning concepts and techniques. The course includes supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms, all to prepare you for assuming the role of Machine Learning Engineer.
Even if you’re not planning on a career in machine learning, it’s an excellent course to take for those who want to upskill and increase their marketability. After all, areas of knowledge such as data mining techniques and machine learning applications will always be in demand. And knowing these disciplines can add to your versatility as a digital professional.
You can choose between self-paced learning, the online classroom Flexi-pass, or as a corporate training solution. You’ll get over 40 hours of instructor-led training, over two dozen hands-on exercises, four real-life industry projects with integrated labs, and 24×7 support with dedicated project mentoring sessions.
Once you’ve passed the criteria, you’ll earn your certification, which is your ticket to this fantastic field. Check it out now, and secure your future digital career!
You can also take-up the AI and Machine Learning certification courses in partnership with Purdue University collaborated with IBM. This program gives you an in-depth knowledge of Python, Deep Learning with the TensorFlow, Natural Language Processing, Speech Recognition, Computer Vision, and Reinforcement Learning.
The comprehensive Post Graduate Program provides you a joint Simplilearn-Purdue certificate, and also, you become entitled to membership at Purdue University Alumni on course completion. IBM is the leading player in AI and Data Science, helping professionals with relevant industry exposure in the field of AI and Data Science, providing a globally recognized certificate, complete access to IBM Watson for hands-on learning and practice. The game-changing PGP program will help you stand in the crowd and grow your career in thriving fields like AI, machine learning and deep learning.
What Is The Difference Between Data Mining And Machine …
What Is The Difference Between Data Mining And Machine Learning? | Bernard Marr
Skip to content
Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has over 2 million social media followers, 1 million newsletter subscribers and was ranked by LinkedIn as one of the top 5 business influencers in the world and the No 1 influencer in the UK.
Bernard’s latest book is ‘Business Trends in Practice: The 25+ Trends That Are Redefining Organisations’
The huge leaps in Big Data and analytics over the past few years has meant that the average business user is now grappling with a whole new lexicon of tech-terminology. This can breed confusion, as people aren’t sure of the difference between terms and approaches. In my experience, ‘data mining’ and ‘machine learning’ are a prime example of this.
In this article, I define both data mining and machine learning, and set out how the two approaches differ. So if you’ve never quite grasped the difference, this article is for you.
What is data mining?
Data mining is a subset of business analytics and refers to exploring an existing large dataset to unearth previously unknown patterns, relationships and anomalies that are present in the data. It gives us the ability to find completely new insights that we weren’t necessarily looking for – unknown unknowns, if you like.
For example, if a business has a lot of data on customer churn, it could apply a data mining algorithm to find unknown patterns in the data and identify new associations that could indicate customer churn in the future. In this way, data mining is frequently used in retail to spot patterns and trends.
What is machine learning?
Machine learning is a subset of artificial intelligence (AI). With machine learning, computers analyse large data sets and then ‘learn’ patterns that will help it make predictions about new data sets. Apart from the initial programming and maybe some fine-tuning, the computer doesn’t need human interaction to learn from the data.
Put simply, machine learning is about teaching computers to learn a bit like humans do, by interpreting information and learning from our successes and failures. As an analytic process, it’s particularly useful for predicting outcomes. So, Netflix predicting you may want to watch Ozark next, based on the viewing preferences of other users with similar profiles, is an example of machine learning in action. Real-time fraud detection on credit card transactions is another example.
Why do people confuse the two?
As you can see, there are some similarities between the two concepts:
Both are analytics processes
Both are good at pattern recognition
Both are about learning from data so that we can improve decision making
Both require large amounts of data to be accurate
In fact, machine learning may use some data mining techniques to build models and find patterns, so that it can make better predictions. And data mining can sometimes use machine learning techniques to produce more accurate analysis.
What are the key differences?
Data mining and machine learning may, at heart, both be about learning from data and making better decisions. But the way they go about this is different. Here are some of the key differences between the two:
While data mining is simply looking for patterns that already exist in the data, machine learning goes beyond what’s happened in the past to predict future outcomes based on the pre-existing data.
In data mining, the ‘rules’ or patterns are unknown at the start of the process. Whereas, with machine learning, the machine is usually given some rules or variables to understand the data and learn.
Data mining is a more manual process that relies on human intervention and decision making. But, with machine learning, once the initial rules are in place, the process of extracting information and ‘learning’ and refining is automatic, and takes place without human intervention. In other words, the machine becomes more intelligent by itself.
Data mining is used on an existing dataset (like a data warehouse) to find patterns. Machine learning, on the other hand, is trained on a ‘training’ data set, which teaches the computer how to make sense of data, and then to make predictions about new data sets.
Clearly, there are some distinct differences between the two. Yet, as businesses look to become more and more predictive, we may see more overlap between machine learning and data mining in future. For example, more businesses may seek to improve their data mining analytics with machine learning algorithms.
Where to go from here
If you would like to know more about Machine Learning, AI and Big Data, cheque out my articles on:
What Is Machine Learning – A Complete Beginner’s Guide
What Is The Difference Between Artificial Intelligence And Machine Learning?
What Are Artificial Neural Networks – A Simple Explanation For Absolutely Anyone
What is Deep Learning AI? A Simple Guide With 8 Practical Examples
Or browse other related articles.
How Artificial Intelligence Can Help Small Businesses
Small and medium-sized businesses all over the world are benefiting from artificial intelligence and machine learning – and integrating AI into core business functions and processes is getting more accessible and more affordable every day. [… ]
1234
Get updates straight to your inbox
Join my 1 million newsletter subscribers
Never miss any new content
Data Mining Vs. Machine Learning: The Key Difference
Our rapidly growing digital world has popularized so many new terms and phrases that it’s easy to get overwhelmed or lose track. The onslaught of technobabble is overwhelming. And people are liable to use strange new words interchangeably, unaware that the words mean two different things.
Specifically, that’s the issue facing “data mining” and “machine learning. ” The line between the two terms sometimes gets blurred due to some shared characteristics. In this article we will cover the following topics that will give you a clear understanding of the difference between data mining and machine learning:
What is data mining?
What is machine learning?
Similarities between data mining and machine learning
Differences between data mining and machine learning
Enhance your AI skill set and give a boost to your career with the Artificial Intelligence Course.
What is Data Mining?
Data mining is considered the process of extracting useful information from a vast amount of data. It’s used to discover new, accurate, and useful patterns in the data, looking for meaning and relevant information for the organization or individual who needs it. It’s a tool used by humans.
What is Machine Learning?
On the other hand, machine learning is the process of discovering algorithms that have improved courtesy of experience derived from data. It’s the design, study, and development of algorithms that permit machines to learn without human intervention. It’s a tool to make machines smarter, eliminating the human element (but not eliminating humans themselves; that would be wrong).
What Do They Have in Common?
Both data mining and machine learning fall under the aegis of Data Science, which makes sense since they both use data. Both processes are used for solving complex problems, so consequently, many people (erroneously) use the two terms interchangeably. This isn’t so surprising, considering that machine learning is sometimes used as a means of conducting useful data mining. While data gathered from data mining can be used to teach machines, the lines between the two concepts become a bit blurred.
Furthermore, both processes employ the same critical algorithms for discovering data patterns. Although their desired results ultimately differ, something which will become clear as you read on.
Difference between Data Mining and Machine Learning
So we see that their similarities are few, but it’s still natural to confuse the two terms because of the overlap of data. On the other hand, there’s a considerable number of differences between the two. So for the sake of clarity and organization, we are going to give each one its bullet item.
Let’s dig in to find out some of the differences between data mining and machine learning:
Their Age
For starters, data mining predates machine learning by two decades, with the latter initially called knowledge discovery in databases (KDD). Data mining is still referred to as KDD in some areas. Machine learning made its debut in a checker-playing program. Data mining has been around since the 1930s; machine learning appears in the 1950s.
Their Purpose
Data mining is designed to extract the rules from large quantities of data, while machine learning teaches a computer how to learn and comprehend the given parameters. Or to put it another way, data mining is simply a method of researching to determine a particular outcome based on the total of the gathered data. On the other side of the coin, we have machine learning, which trains a system to perform complex tasks and uses harvested data and experience to become smarter.
What They Use
Data mining relies on vast stores of data (e. g., Big Data), which then, in turn, is used to make forecasts for businesses and other organizations. Machine learning, on the other hand, works with algorithms, not raw data.
The Human Factor
Here’s a rather significant difference. Data mining relies on human intervention and is ultimately created for use by people. Whereas machine learning’s whole reason for existing is that it can teach itself and not depend on human influence or actions. Without a flesh and blood person using and interacting with it, data mining flat out cannot work. Human contact with machine learning, on the other hand, is pretty much limited to setting up the initial algorithms. And then just letting it be, a sort of “set it and forget it” process. People babysit data mining; the systems take care of themselves with machine learning.
How They Relate to Each Other
Also, data mining is a process that incorporates two elements: the database and machine learning. The former provides data management techniques, while the latter supplies data analysis techniques. So while data mining needs machine learning, machine learning doesn’t necessarily need data mining. Though, there are cases where information from data mining is used to see connections between relationships. After all, it’s hard to make comparisons unless you have at least two pieces of information that compare against each other! Consequently, information gathered and processed via data mining can then be used to help a machine learn, but again, it’s not a necessity. Think of it more as a convenience that’s handy to have.
The Ability to Grow
Here’s an easy one: data mining can’t learn or adapt, whereas that’s the whole point with machine learning. Data mining follows pre-set rules and is static, while machine learning adjusts the algorithms as the right circumstances manifest themselves. Data mining is only as smart as the users who enter the parameters; machine learning means those computers are getting smarter.
How They Are Used
In terms of utility, each process has its specialty carved out. Data mining is employed in the retail industry to fathom their customers’ buying habits, thereby helping businesses formulate more successful sales strategies. Social media is a fertile playground for data mining, as gathering information from user profiles, queries, keywords, and shares can be brought together. It will help advertisers put together relevant promotions. The world of finance uses data mining for researching potential investment opportunities and even the likelihood of a startup’s success. Gathering such information helps investors decide if they want to commit money to new projects. If data mining was perfected back in the mid-90s, it could very well have prevented the excellent Internet startup collapse of the late 90s.
Meanwhile, companies use machine learning for purposes like self-driving cars, credit card fraud detection, online customer service, e-mail spam interception, business intelligence (e. g., managing transactions, gathering sales results, business initiative selection), and personalized marketing. Companies that rely on machine learning include heavy hitters such as Yelp, Twitter, Facebook, Pinterest, Salesforce, and a little search engine you may have possibly heard of: Google.
Accelerate your career with the AI and Machine Learning Certification Courses with Purdue University collaborated with IBM.
So What Does This All Mean?
Every day, a little more of our world turns to digital solutions to handle tasks and solve problems. It’s a big enough digital world out there’s more than sufficient room for both data mining and machine learning to thrive. The continued dominance of Big Data means that there will always be a need for data mining. And the continued drive and demand for smart machines will ensure that machine learning remains a very much in-demand skill.
Which offers the most potential, you may wonder? There is no clear cut answer, but we can make a decent, informed guess. The increased interest in artificial intelligence and smart devices and the continued rise in the use of mobile devices are good signs. Between the two processes, machine learning may offer the best opportunities.
That doesn’t mean that data mining is, by any means, a dead-end career. According to Forbes, the total accumulated data in our digital universe will grow from 2019’s total of 4. 4 zettabytes to approximately 44 zettabytes or 44 trillion gigabytes of data. Yes, notice the missing decimal point between those two values!
Want to Get in on Machine Learning?
If you’re looking for an excellent career choice, you can’t miss a job in the field of machine learning. The demand (and salaries! ) for machine learning engineers is on the rise. The average salary of a Machine Learning Engineer is around $146K, with a growth rate last year of 344p percent!
If you want to become a part of this exciting, dynamic world, then Simplilearn has the tools to get you started on your way. The Machine Learning Certification Course will make you an expert in machine learning. You will master machine learning concepts and techniques. The course includes supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms, all to prepare you for assuming the role of Machine Learning Engineer.
Even if you’re not planning on a career in machine learning, it’s an excellent course to take for those who want to upskill and increase their marketability. After all, areas of knowledge such as data mining techniques and machine learning applications will always be in demand. And knowing these disciplines can add to your versatility as a digital professional.
You can choose between self-paced learning, the online classroom Flexi-pass, or as a corporate training solution. You’ll get over 40 hours of instructor-led training, over two dozen hands-on exercises, four real-life industry projects with integrated labs, and 24×7 support with dedicated project mentoring sessions.
Once you’ve passed the criteria, you’ll earn your certification, which is your ticket to this fantastic field. Check it out now, and secure your future digital career!
You can also take-up the AI and Machine Learning certification courses in partnership with Purdue University collaborated with IBM. This program gives you an in-depth knowledge of Python, Deep Learning with the TensorFlow, Natural Language Processing, Speech Recognition, Computer Vision, and Reinforcement Learning.
The comprehensive Post Graduate Program provides you a joint Simplilearn-Purdue certificate, and also, you become entitled to membership at Purdue University Alumni on course completion. IBM is the leading player in AI and Data Science, helping professionals with relevant industry exposure in the field of AI and Data Science, providing a globally recognized certificate, complete access to IBM Watson for hands-on learning and practice. The game-changing PGP program will help you stand in the crowd and grow your career in thriving fields like AI, machine learning and deep learning.
Frequently Asked Questions about machine learning and data mining
Is data mining used in machine learning?
In fact, machine learning may use some data mining techniques to build models and find patterns, so that it can make better predictions. And data mining can sometimes use machine learning techniques to produce more accurate analysis.
What is the difference between data mining and machine learning?
Data mining is designed to extract the rules from large quantities of data, while machine learning teaches a computer how to learn and comprehend the given parameters. Or to put it another way, data mining is simply a method of researching to determine a particular outcome based on the total of the gathered data.5 days ago
What is machine learning approach in data mining?
Machine learning leverages data mining and computational intelligence algorithms to improve decision making models. Example applications of data mining and machine learning to business uses include: … Analyzing demographic and health data to predict profitability of a future drug if it were brought to market.
