Data mining and machine learning are two different areas in artificial intelligence and big data. They complement each other and come together in business intelligence protocols. They both have a lot in common, yet arrive at different ends. So, it’s not really a case of machine learning vs. data mining.
Both data mining and machine learning are popular in marketing, logistics, credit card and fraud detection, e-commerce, and retail. Data scientists and data engineers use both to help businesses.
For example, both machine learning and data mining enable efficient inventory management, quality control, and operational efficiency with little to no human intervention.
There's a lot of overlap when it comes to machine learning and data mining, and people use it interchangeably. But it's important to understand the differences as you will use different technology stacks, processes, and architectures depending on your goals and resources.
When you use both machine learning and data mining properly, you're on the right track to turning raw data into valuable insights that impact your bottom line. These insights can be operational, strategic, or statistical.
For example, in a warehouse, we use data mining and pattern recognition to solve picker routing problems and batching problems. In this scenario, data mining leverages machine learning techniques to accurately estimate the length of the shortest possible route to boost efficiency.
Intrigued? Let’s dive into the similarities and differences between machine learning and data mining.
Machine learning is a part of artificial intelligence (AI) that awards systems the ability to learn and improve automatically based on experience. In this scenario, we can build complex algorithms that process large data sets and use them to learn for themselves without explicit programming.
Machine learning leverages complex linear algorithms that learn through experience and make predictions. These smart algorithms are in a constant state of improvement through the regular input of training data. The primary goal is to explore and understand the data and build models that learn the relationships between data points.
We can break down machine learning into two different types: supervised machine learning and unsupervised machine learning.
Supervised learning is like a student and teacher learning in a classroom. In this case, we know the relationships between the inputs and outputs. Smart algorithms predict the outcome of the input data and compare it to the expected result. Whenever there’s an error, it’s corrected iteratively until an acceptable level of performance is realized.
Unsupervised learning is like a student learning on their own. It relies on training data sets (where the output is known), but you don't have to label it. It also uses direct techniques like clustering algorithms and associations to predict outcomes based on the data.
Data mining or knowledge discovery process describes the practice of digging through data sets. This approach is a popular technique to discover unknown patterns and trends. Such knowledge discovery in databases goes far beyond a simple analysis. This means that data mining extracts usable data from a more extensive set of raw data. Whenever you do this, you can establish relationships and patterns that solve business problems.
Data mining is at the heart of artificial intelligence, machine learning, deep learning, and statistics. While it came to prominence over the last 30 years, it has a history of over 200 years. Data scientists use data mining techniques to find hidden but useful patterns in large databases. We can't address these questions through basic query and reporting techniques.
As data grows rapidly and exponentially, we must use these methods for predictive and useful analysis. Machine learning techniques help quickly process the data and automatically derive results much faster. Data mining techniques highlight patterns and trends in historical data sets to predict future outcomes. These outcomes take the form of charts, graphs, and more.
We use both machine learning and data mining (which fall under data science) to solve complex problems. Machine learning is also used to conduct data mining exercises. The relationships mapped from data mining will teach algorithms, blurring the lines between both concepts.
Both machine learning and data mining use the same algorithms to discover data patterns, but their results will differ. So, with so much in common, we must forgive people for using the terms interchangeably.
The key difference between machine learning and data mining is the level of human intervention that is needed to complete a task. Machine learning, based on artificial intelligence, is a computer that replaces humans to a certain degree to complete a task.
This is because human engagement is required across multiple steps in the process to create a polynomial function that learns relationships, sometimes better than humans.
In contrast, data mining demands human intervention to complete a task. In this scenario, data professionals use tools to extract and discover useful patterns in the data. In this case, there's quite a lot of room for human error.
In comparison, the results generated through machine learning are often far more accurate compared to data mining.
Machine learning uses predictive models, statistical algorithms, and neural networks to get the job done. Data mining uses data warehouses and pattern evaluation techniques to find valuable insights.
You can find key differences in their application, concepts, implementation, learning capability, and scope.
Machine learning algorithms demand information in a standard data format. To analyze the data with machine learning, you must move the data sources from their native formation to a standard format. This will help intelligent algorithms quickly understand the information.
Machine learning also requires an enormous amount of data to deliver accurate results. Data mining can also produce results but on a lower volume of data.
Machine learning algorithms run on the concept that machines learn from existing data. This approach also helps it improve itself. Machine learning develops models based on the logic behind the data. This helps predict future outcomes (using data mining methods and algorithms).
We build these algorithms based on mathematics and programming languages like Python. Data mining concentrates on extracting information using techniques that help identify patterns and trends in the data.
We can implement machine learning by leveraging smart algorithms in linear regression, decision trees, neural networks, and more. Machine learning essentially uses automated algorithms and neural networks to predict outcomes.
When it comes to data mining, we must build models using databases, data mining engines, and pattern evaluation techniques.
Machine learning uses the same techniques as data mining, but the former is automated. This means that machine learning automatically learns, adapts, and changes. As a result, it’s more accurate than data mining when it comes to making predictions. In contrast, data mining demands human analysis, making it a manual method.
Machine learning is often used to make predictions like dynamic pricing optimization. In this scenario, it automatically learns the model over time and provides real-time feedback.
For example, Uber's "surge pricing" follows a dynamic pricing model that ensures a balance between supply and demand. By raising the price when there's a significant increase in demand for rides, the company prices some customers out while making it more lucrative for the drivers in real-time.
When starting a new data project, it’s best not to think of it as machine learning vs. data mining. Instead, look at your available data and project goals.
If you look at use cases about how machine learning algorithms work on social media platforms, you'll see that machine learning models start by classifying nodes (or a basic unit of a data structure) based on user data. This could be anything like education, political affiliation, and so on.
By building link lists and data tree structures, statistical algorithms match what users have in common and make recommendations.
However, some of these associations look at sensitive user attributes like gender and skin color.
In another use case, researchers used data mining to better understand the severity of bus crashes in the State of Victoria, Australia, between 2006-2019. They divided the clustering of crash data into homogeneous categories. Next, they went on to add association rules on the clusters.
Once complete, factors that affected fatalities in bus crashes were extracted. The results showed that the highest fatality rates were directly related to bus crashes with vehicles. Another major contributor was weekend crashes.
Since this study used a small data set, researchers could get valuable insights through data mining.
Using machine learning and data mining together is a wise financial move. It helps optimize operations, minimize errors, reduce accidents, and find a perfect balance between supply and demand.
To learn more about machine learning and data mining and how they can be applied to your next project, contact us today.
