data-mining-vs-machine-learning

Data Mining vs Machine Learning: Which is Important For Data Science?

Why do students search for data mining vs machine learning? The most common reason for this is that both of these have a significant role in data science. Data mining uses the collected data to get useful patterns using modern technologies. On the other hand, ML (machine learning) uses to train the data by which the computer can sense the data to predict useful results.

Both data mining vs machine learning is searched because several students are confused with their functionalities. At some point, both have a few similarities. That is why I am here to clear all your doubts regarding data mining and machine learning.

But before proceeding to the details, let’s check the trending graph of both. After this, I will explain the relevant differences among these.

An Overview of data mining and machine learning

Data mining

It is the business analytics subset that use to explore the unknown patterns of an existing large dataset. It provides new insights that play a significant role in data science and deep learning applications.

See also  Key Points on the Importance of Data Science for Business

Let’s take an example of it:

Suppose a business has various customer data. The owner of the company wants to get valuable patterns to make better decisions. For it, data mining is used to spot relevant trends and patterns.

Machine Learning

This AI (artificial intelligence) subset helps the computer analyze the data and learn the patterns to make valuable predictions. Or simply, I can say that ML can teach computers to learn things as humans by interpreting the data. 

Let’s take an example of it:

Netflix predicts that you should watch Ozark next. This prediction is made as per the viewing preferences of another Netflix user with the same profile as you. This happens just because of machine learning.

Trending Graph 2021: Data mining vs machine learning

From the trending graph, it is quite clear that machine learning has more popularity. On the other hand, data mining is also in demand but not as much as machine learning.

Apart from this, data mining has some kind of steady popularity pattern. At the same time, machine learning popularity kept on increasing over time. But in 2020, there is a slight drop in its popularity.

Is there any similarity between data mining and machine learning?

Yes, there is!!

Below are some of the similarities which you can see between data mining and machine learning. Let’s check them one by one:

See also  Business Analytics vs Data Science: All You Need to Know
Both of these relate to the information extraction that can use for specific purposes.
These terms are listed among the trending words. But they get everyone’s attention when Facebook and Google, like giants, start using these technologies.
These use algorithm approaches to filter through tools, data, and applications.
Both of these deal with algorithms, mathematics, and statistics.
Sometimes, both of them use the same structural or algorithmic approaches.

Data mining vs machine learning: What are the key differences among these?

S. No.Data MiningMachine Learning
Data mining is the process of extracting valuable information from a large dataset.Machine learning introduces the algorithms to train the computer by analyzing past things.
It is useful for large databases with unstructured data.It uses the existing data along with the algorithms.
Because of human interference, the chances of error are more.There is no need for human effort once the algorithm is designed.
The data mining accuracy is based on the methods of data collection.ML produces more accurate results as compared to data mining.
It needs a research-based approach.Self-learning, as well as training methods, are considered to perform intelligent tasks.
It is used for understanding the data flow.It helps the computer to learn & understand the useful things from the data flow.
Models can improve by applying data mining techniques.Machine learning techniques and algorithms are used in neural networks, decision trees, and some areas of artificial intelligence.
It can deal with fewer data compared to machine learning.To get more accurate results, it needs a large amount of data.
It abstracts all the useful details from the data warehouse.Machine learning can easily read machines.
It has applications in cluster analysis.It has applications in web Search, fraud detection, spam filter, and computer design.

List of best tools used for data mining and machine learning

Data miningMachine Learning
RapidMiner StudioKNIME
Sisense for Cloud Data TeamsWeka
SAS Visual Data MiningTensorFlow
Alteryx DesignerScikit Learn
TIBCO Data SciencePyTorch

Also Read

See also  What Are The Interesting Applications Of Data Science?

Which is better, data mining or machine learning?

If we talk about the accuracy of data mining vs machine learning, then we can say that machine learning algorithms can provide more accurate results as compared to data mining. The reason for this is that data mining needs human intervention. Due to this, there is always a possibility of missing important relationships.

On the other hand, machine learning utilizes data mining methods to enhance the algorithms. Therefore, we can say that data mining considers being the input source for ML. Hence, it is important to note that data mining is also important for the better performance of ML. 

Finally, it can be seen that both have their own uses and advantages that vary as per your requirement. That is why you can select any of these as per your preference of work.

Bonus Point

Data mining vs machine learning: Which is the best option in terms of a salary package?

You can see that machine learning has more popularity. That is why it also provides better job opportunities with a high salary package.
Below, I have compared both salary packages. This is the average salary package provided in the United States. It can vary from country to country.

If you have data mining skills, even then, you can easily earn a good salary package. That is why if you want to pursue your career in this field, then go ahead.  Your salary can also improve as per your experience in the field. 

Conclusion

Data mining vs machine learning had various differences along with some of the similarities. Data mining uses ML algorithms to enhance the accuracy and analysis’ depth. At the same time, ML can employ the mined data to get excellent results.

In this blog, I have detailed all the major differences between data mining and machine learning. Still, if you have any doubt regarding both, then let me know through your comments. I will provide you with the best solutions in the best possible way. Get the best Quantitative Methods Assignment Help from our experts

Frequently Asked Questions

Is data mining important for machine learning?

Data Mining is one of the crucial parts of Machine Learning. This is used for finding useful trends and patterns hidden within a large data volume.

What are the data mining techniques?

Some of the data mining techniques are:

Tracking patterns.  
Association. 
Classification.
Clustering.
Prediction.
Outlier detection. 
Regression.

What are some examples of machine learning?

Some of the examples of machine learning are:

Recommendation Engines (Netflix)
Self-Driving Cars (Waymo)
Sorting, tagging, and categorizing photos (Yelp)
Customer Lifetime Value (Asos)
Education (Duolingo)
Patient Sickness Predictions (KenSci)
Targeted Emails (Optimail)