Data Mining Vs. Machine Learning

Data mining

Data mining is a subset of business analytics and refers to exploring an existing large dataset to unearth previously unknown patterns, relationships and anomalies that are present in the data. It gives us the ability to find completely new insights that we weren’t necessarily looking for – unknown unknowns, if you like.

For example, if a business has a lot of data on customer churn, it could apply a data mining algorithm to find unknown patterns in the data and identify new associations that could indicate customer churn in the future. In this way, data mining is frequently used in retail to spot patterns and trends.

Machine learning

Machine learning is a subset of artificial intelligence (AI). With machine learning, computers analyse large data sets and then ‘learn’ patterns that will help it make predictions about new data sets. Apart from the initial programming and maybe some fine-tuning, the computer doesn’t need human interaction to learn from the data.Put simply, machine learning is about teaching computers to learn a bit like humans do, by interpreting information and learning from our successes and failures. As an analytic process, it’s particularly useful for predicting outcomes. 

For Example,Netflix predicting you may want to watch Ozark next, based on the viewing preferences of other users with similar profiles, is an example of machine learning in action. Real-time fraud detection on credit card transactions is another example.

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 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 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 its own means once implemented forever to use but this is not the case with data mining.
• The result produced 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 models and automated algorithms to make the decisions.

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Below are the lists of points, describe the comparison between Data Mining and Machine Learning

Data mining and Machine Learning fall under the same world of Science. Though these terms are confused with each other, there are some major differences between them.

1) Scope: Data Mining is used to find out how different attributes of a data set are related to each other through patterns and data visualization techniques. The goal of data mining is to find out the relationship between 2 or more attributes of a data set and use this to predict the outcomes or actions.

Machine Learning is used for making predictions of the outcome such as price estimate or time duration approximation. It automatically learns the model with experience over time. It provides real-time feedback.

2) Function: Data Mining is the technique of digging deep into data to take out useful information. Whereas Machine Learning is a method of improving complex algorithms to make machines near to perfect by iteratively feeding it with the trained dataset.

3) Uses: Data Mining is more often used in the research field while machine learning has more uses in making recommendations of the products, prices, time, etc.

4) Concept: The concept behind data mining is to extract information using techniques and find out the trends and patterns.

Machine Learning runs on the concept that machines learn from the existing data and improves by itself. Machine learning uses data mining methods and algorithms to build models on the logic behind data which predict the future outcome. The algorithms are built on Maths and programming languages.

5) Method: Machine Learning uses the data mining technique to improve its algorithms and change its behavior to future inputs. Thus data mining acts as an input source for machine learning.

Machine learning algorithms will continuously run and improve the performance of the system automatically, and also analyze when the failure can occur. When there is some new data or change in the trend, the machine will incorporate the changes without the need to reprogram or any human interference.

Data mining will perform analysis in the Batch format at a particular time to produce results rather than on a continuous basis.

6) Nature: Machine Learning is different from Data Mining as machine learning learns automatically while data mining requires human intervention for applying techniques to extract information.

7) Learning Capability: Machine Learning is a step ahead of data mining as it uses the same techniques used by data mining to automatically learn and adapt to changes. It is more accurate than data mining. Data Mining requires the analysis to be initiated by human and thus it is a manual technique.

8) Implementation: Data mining involves building models on which data mining techniques are applied. Models like the CRISP-DM model are built. Data mining process uses a database, data mining engine and pattern evaluation for knowledge discovery.

Machine Learning is implemented by using Machine Learning algorithms in artificial intelligence, neural network, neuro-fuzzy systems, and decision tree, etc. Machine learning uses neural networks and automated algorithms to predict the outcomes.

9) Accuracy: Accuracy of data mining depends on how data is collected. Data Mining produces accurate results which are used by machine learning and thereby makes machine learning produce better results.

As data mining requires human intervention, it may miss important relationships. Machine learning algorithms are proved to be more accurate than the Data Mining techniques.

10) Applications: Machine learning algorithm needs data to be fed in a standard format, due to which the algorithms available are much limited. To analyze data using machine learning, data from multiple sources should be moved from native format to standard format for the machine to understand.

It also requires a large amount of data for accurate results. This is an overhead when compared to data mining.

11) Examples: Data mining is used in identifying sales patterns or trends while machine learning is used in running marketing campaigns.

Conclusion

Data becomes the most important factor behind machine learning, data mining, data science, and deep learning. The data analysis and insights are very crucial in today’s world. Hence investing time, effort, as well as costs on these analysis techniques, forms a critical decision for businesses.

As data is growing at a very fast pace, these methods should be fast enough to incorporate the new data sets and predict useful analysis. Machine learning can help us to quickly process the data and deliver quicker results in the form of models automatically.

Data mining techniques produce patterns and trends from historical data to predict future outcomes. These outcomes are in the form of graphs, charts, etc. Statistical analysis forms an integral part of data analysis and will grow higher in the near future.

These technologies will immensely grow in the future as business processes improve. These, in turn, will also help the businesses to automate the manual process, increase sales and profits, and thereby help in customer retention.