This article Difference between data mining and machine learning will provide you 17 differences between data mining and machine learning or Data mining Vs Machine Learning. Today’s tons of data is generated. We even don’t realize where this data goes actually. The data which is generated is not structured, even it is unstructured. To find valuable insights from this kind of data, some techniques are deployed. Data mining is one of those techniques, which helps in mining the data. Whereas machine learning is a way of learning from past experiences and needs not to be programmed. In this blog, we will discuss the 17 main differences between data mining and machine learning.
“Data mining is the process of exploring the data from large chunks of the dataset , say, from the warehouse to find valuable information from it.”
And
“Machine learning is the process of teaching computers to execute a particular task and do not required to give explicit instructions, the computer can learn from past experiences.”
Difference between data mining and machine learning is as under:-
| Features | DATA MINING | MACHINE LEARNING |
| 1. Definition | The process of discovering patterns in large chunks of data that makes the involvement of various machine learning methods and statistical methods are called data mining | The scientific study of algorithms and different statistical models that computer systems assist to perform a specific task without make use of explicit instructions, focusing on patterns is called machine learning. |
| 2. Process | Extracting knowledge from large data sets | Introducing new algorithms for working |
| 3. Originality | Traditional Databases which possess unstructured data | Present data and various algorithms |
| 4. Techniques | It involves the method of machine learning | It involves self-learning methods |
| 5. Human intervention | Here human intervention is needed | No human intervention is required |
| 6. Results | Results are not as accurate as in the case of machine learning. There are huge sets of data which is unstructured and semi-structured, which lacks accuracy and results are not up to the mark | The results are accurate in this case. |
| 7. History | It came into existence in the year 1930 | It came into existence in the year 1950. |
| 8. Scope | It is used in a limited area | It has a vast scope |
| 9. Self-learning | Self-learning is absent in data mining | Machine learning involves self-learning techniques. |
| 10. Another name | It is also referred to as KDD
Knowledge discovery databases |
Earlier it has made its appearance in checker playing program |
| 11. Dependability | They depend on vast data, you can also say big data | They depend on algorithms |
| 12. Learnability | It is not capable to learn | It can learn from experiences. |
| 13. Accuracy | It relies on machine learning techniques to get accurate results | It generates accurate results |
| 14. Implementation | It focuses on building various models in which data mining techniques are used. For Example, the crisp-dm model is used | Machine learning makes the use of machine learning algorithms like neural networks, decision trees and random forest. |
| 15. Area of uses | Data mining is mostly used in research areas such as web mining etc. | It is mainly deployed in recommendation systems. |
| 16. Applications | It is used in cluster analysis | It is used in spam filtering, web search and fraud detection. |
| 17. Automation | Data mining makes use of some tools to extract relevant data, it does not involve automation | Machine learning involves automation techniques |
Conclusion
People often confuse with data mining and machine learning. Both are not the same terms. But in some way, they are related to each other. In this blog, we have represented 17 differences between data mining and machine learning. If you are having any doubt, feel free to ask me in the comment box.
