Trending September 2023 # Data Mining Vs Machine Learning # Suggested October 2023 # Top 15 Popular |

Trending September 2023 # Data Mining Vs Machine Learning # Suggested October 2023 # Top 15 Popular

You are reading the article Data Mining Vs Machine Learning updated in September 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested October 2023 Data Mining Vs Machine Learning

Difference Between Data Mining and Machine Learning

Data mining, introduced in 1930, involves finding potentially useful, hidden, and valid patterns from large amounts of data. While machine learning introduced in near 1950 involves new algorithms from the data as well as previous experience to train and make predictions from the models, both of them intersect at the point of having useful datasets but other than that, they have various differences based upon the responsibilities, origin, Implementation, Nature, Application, Abstractions, Techniques and scope.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Head to Head comparison Between Data mining and Machine learning (Infographics) Key Differences Between Data Mining and Machine Learning

Let us discuss some of the major differences between Data Mining and Machine Learning:

To implement data mining techniques, it used two-component first one are the database and the second one is machine learning. The Database offers data management techniques, while machine learning offers data analysis techniques. But to implement machine learning techniques, it used algorithms.

Data Mining uses more data to extract useful information, and that particular data will help to predict some future outcomes; for example, a sales company uses last year’s data to predict this sale, but machine learning will not rely much on data. It uses algorithms, for example, OLA and UBER machine learning techniques, to calculate the ETA for rides.

Self-learning capacity is not present in data mining; it follows the rules and is predefined. It will provide the solution for a particular problem, but machine learning algorithms are self-defined and can change their rules as per the scenario; it will find out the solution for a particular problem and resolve it in its own way.

The main and foremost difference between data mining and machine learning is without the involvement of humans, data mining can’t work, but in machine learning, human effort is involved only at the time when the algorithm is defined. After that, it will conclude everything by itself once implemented forever to use, but this is not the case with data mining.

The result produced by machine learning will be more accurate than data mining since machine learning is an automated process.

Data mining uses the database or data warehouse server, data mining engine, and pattern evaluation techniques to extract useful information. In contrast, machine learning uses neural networks, predictive models, and automated algorithms to make decisions.

Data Mining and Machine Learning Comparison Table

Basis for Comparison Data Mining Machine Learning

Meaning Extracting knowledge from a large amount of data Introduce a new algorithm from data as well as past experience

History Introduced in 1930, initially referred as knowledge discovery in databases Introduced in near 1950, the first program was Samuel’s checker-playing program

Responsibility Data mining is used to get the rules from the existing data. Machine learning teaches the computer to learn and understand the given rules.

Origin Traditional databases with unstructured data Existing data as well as algorithms.

Implementation We can develop our own models where we can use data mining techniques for We can use machine learning algorithms in decision trees, neural networks, and some other areas of artificial intelligence.

Nature Involves human interference more manually. Automated, once design is self-implemented, no human effort

Application used in cluster analysis used in web search, spam filter, credit scoring, fraud detection, computer design

Abstraction Data mining abstract from the data warehouse

Techniques Involved Data mining is more of research using methods like machine learning Self-learned and trained system to do the intelligent task.

Scope Applied in the limited area It can be used in a vast area.


In most cases now, data mining is used to predict the result from historical data or find a new solution from the existing data. Most of organization uses this technique to drive business outcomes where machine learning techniques are growing in a much faster way since it overcomes the problems with what data mining techniques have. But to drive the business still, we need to have data mining process because it will define the problem of a particular business, and to resolve such problem, we can use machine learning techniques. In one word, we can say that to drive a business, both Data mining and Machine learning techniques have to work hand in hand, one technique will define the problem, and the other will give you the solution in a much more accurate way.

Recommended Articles

This has been a guide to Data Mining vs Machine Learning. Here we have discussed Data Mining vs Machine Learning head-to-head comparison and key differences, along with infographics and comparison table. You may also look at the following articles to learn more –

You're reading Data Mining Vs Machine Learning

Update the detailed information about Data Mining Vs Machine Learning on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!