Difference Between Data Mining and Machine Learning
Data mining introduce in 1930 involves finding the potentially useful, hidden and valid patterns from large amount 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 dataset but other than that they have various difference based upon the responsibilities, origin, Implementation, Nature, Application, Abstractions, Techniques and scope.
Let us understand Data mining and Machine learning in detail in this post.
Head to Head comparison Between Data mining and Machine learning (Infographics)
Below is the Top 10 Comparision between Data mining and Machine learning:
Key Differences Between Data Mining and Machine Learning
Let us discuss some of the major difference between Data Mining and Machine Learning:
- To implement data mining techniques, it used two-component first one is 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 in a sales company it uses last year data to predict this sale but machine learning will not rely much on data it uses algorithms, for example, OLA, UBER machine learning techniques to calculate the ETA for rides.
- Self-learning capacity is not present in data mining, it follows the rules and 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 it resolves it by its own way.
- The main and foremost difference between data mining and machine learning is, without the involvement of human data mining can’t work but in machine learning human effort is involved only the time when algorithm is defined after that it will conclude everything by own means once implemented forever to use but this is not the case with data mining.
- The result produces by machine learning will be more accurate as compared to 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 the useful information whereas machine learning uses neural networks, predictive model and automated algorithms to make the decisions.
Data mining and Machine learning Comparison Table
Below are the lists of points, describe the comparison between Data Mining and Machine Learning
| 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 algorithm in the decision tree, neural networks and some other area of artificial intelligence. |
| Nature | Involves human interference more towards manual. | Automated, once design 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 | Machine learning reads machine |
| Techniques Involved | Data mining is more of research using methods like machine learning | Self-learned and trains system to do the intelligent task. |
| Scope | Applied in the limited area | Can be used in a vast area. |
Conclusion
In most of the cases now data mining is used to predict the result from historical data or find a new solution from the existing data. Most of the organization uses this technique to drive the business outcomes. Where machine learning techniques are growing in a much faster way since it overcomes the problems with what data mining techniques have. Since Machine learning process is more accurate and less error prone when compared to data mining and it is much more capable to take his own decision and resolve the issue. 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 to hand, one technique will define the problem and other will give you the solution in the much accurate way.
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