Difference between data mining and machine learning

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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.

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