How machine learning is different from data mining

Before starting the difference between Data Mining and Machine Learning, let us first know the concept of data mining and machine learning. Here we will discuss how Machine Learning is different from Data Mining.

Data Mining vs Machine Learning

What is Data Mining

Data Mining is the process of extracting the knowledge from huge data or we can say that it is the steps to look out different types of patterns which are acknowledged in the information with accuracy and usefulness. It is the repeated process for the creation of a descriptive and assumed model without showing the earlier trends or patterns of wide and large data for making decisions.

It is known as the business analytics which is very similar to research by experiments. The data mining came from statistics and databases whereas machine learning integrates the process which is deployed automatically via experience totally based on data.

What is Machine learning

Generally, we can also state that machine learning is the steps for searching out new algorithms or processes from the experience. It is the learning of algorithms as well which can be used for getting the data automatically. Machine learning source is also data which is technically known as databases where it has two data sets such as techniques of mining and algorithm of learning for building models of what is happening to that data for assuming the results from it.

Now let us just have a look at Data Mining and Machine learning differences in detail.

Key Difference between Data-Mining and Machine Learning

Data mining and Machine learning both are supreme data driven disciplines that help to make better decisions.

deep learning of AI, ML and data mining

  • For the implementation of the techniques of data mining, it uses two components — first is the database and the other is machine learning. The database provides the techniques of data management whereas machine learning provides the techniques of data analysis but for implementation of machine learning methods, it uses processes.
  • Data mining is useful for extracting useful information from huge data and that respective data will be helpful for assuming the future results and for instance, in the company of sales, it uses some previous year data for predicting this sale but machine learning will not depend on data where it uses algorithms like UBER, OLA techniques of machine learning for evaluating ETA.
  • The self-learning ability is absent in data mining, which follows the rules and predefined conditions which will offer the solution for the appropriate issue but algorithms of machine learning are self-defined and can change their rules according to the situation or requirement, it will also determine the answer for the particular issue and it also solve it out by their own way.
  • The key difference between these both machine learning and data mining is that without any engagement of manual mining of data cannot work effectively but in machine learning, manual efforts and hard work are engaged at the time when the process is defined after which it will show all the things by its own whenever executed once for using, but it is not always same with mining of data where machine learning is an automatic algorithm or say steps.
  • Mining of data uses the database or warehouse of the database server, where data engine and its evaluation pattern process used for extracting the useful data and neural networks are used by machine learning with its assumed models and automatic process for decision making.

Comparison table between data mining and machine learning

Features Data Mining Machine Learning
Introduction To get the knowledge from very huge data Introducing the new steps from data as well as extracting information.
Previous time It was introduced in 1930 which is known as the discovery of knowledge in databases It was introduced in the year of 1950.
It’s roles It is useful for getting the rules from already occurring data It also provides teaching on the computer for learning and developing the skills.
Sources Traditional warehouse with unstructured data Already occurring data and its processes.
Execution We can create our own fine and good models where we can use the mining of data methods We can use its processes for the making of a decision in other fields of AI.
It’s nature It engages manual interfering. It provides automated and self-designed execution
Real-time It is useful for analysis of clusters It is useful for searching we, spam filter, detection of frauds
It’s abstract It is useful for extracting data from the storehouse of data It is useful for reading machines
Techniques It uses methods such as machine learning Trained and self-learning system to involve some intelligence
Its future scope It is applicable in some limited area It can be useful for wide areas.

How is it useful?

  1. In many of the cases, mining of data is useful for assuming the outcome from the historical data or determining the new answer from the already existing information. Most of the companies or organizations use such a process for driving the outcomes of the business. whereas machine learning is rising so quickly where it overcomes the issues with the techniques of mining of data.
  2. Though mining of data is very highly accurate and has less mobile errors in comparison to the mining of data, it is able to understand this process of data mining as it will show you the issue of the respective business for resolving such issues. We can utilize the techniques of machine learning. We can just say that for driving the business to grow, both minings of data as well as machine learning have the work hand to hand. There is a technique for defining the issue and another one will just offer you the answer in an efficient way.
  3. Mining of data is not the new discovery which arises in this age of the digital era. As per the hacker bits, the latest mining of data when data scientist Alan Turing just declared the universal machine’s idea which can evaluate the computation techniques which is the same as those of the latest computers.
  4. This test checks that the computer has intelligence accuracy or not. For passing the test, a PC is required for fooling the human being into believing that it also was a living being. After two to three years, there is the creation of a program for the world’s first program of self-learning. It was seen that it takes and gets best at winning the best moves’ studying.

As an outcome, data analytics have just now become the essential employees at the companies all across the globe for grabbing higher success.

Benefits of data mining and machine learning

With data being so popular in the world of business, many of the data words seems to be just thrown away with a clear and deep understanding of what they mean. Both machine learning, as well as data mining, are deeply based on data science and commonly come under the umbrella. They are very much in doubt with each other or intersect with each other, still, there are very few key differences between these two. Now just have a view at the mining of data and machine learning.

Usage of data

The main difference between data mining and machine learning is the method to use it and apply to our daily lives. For instance, mining of data is very much used for seeing the connections. OLA or Uber utilize machine learning for evaluating ETA to ride and deliver meals.

Mining of data can be useful for the purposes, which involves research of finance. Investors may use a web scraping tool to mine the web to see the start-ups or financials of their portfolio companies which is helpful for identifying if they wish to provide funding. The company might also use the mining of data for helping collect data on trends of sales for the best information from its marketing to the needs of inventory and secure the new leads.

Mining of data can be useful for combing via profiles of social media, digital assets, and websites for compiling the data on the ideal of the organization to begin the campaign. Using mining of data can go to many of the leads within a few minutes. And with this data, data analytics can also assume the trends of the future process which will help the organization prepare very high for the clients who wish that in the years and months to arrive.

Machine learning integrates the data mining principles but can even make the automated relation and practice from it to apply it for new processes. It is the new technology beyond cars which are self-driven which can adjust to the new situation very fast during driving the car or vehicle. It also offers higher suggestions when a user buys amazon’s products. These processes or analytics are for the improvement, thus, the outcome will be vital and effective according to time. It is not AI, but it has the skill to improve and learn which is an essential aspect.

Even banks are also using it in machine learning for helping you to see fraud detection while swiping the credit cards by shops or malls. It is helpful for determining the frauds of financial issues in the real-time world online or offline to the transaction of banks. This new and latest technology is useful for the rapid determination of frauds and can be helpful for the protection of retailers to their activity of finance.

Learning basement

Machine learning as well as data mining grabs the similar basement but in various other ways. The data scientists use such pulls of data mining from already occurring information to see the emergence of patterns which can be helpful to shape the processes of decision making. The people who use brands free of clothing, for instance, use the mining of data for combing via many clients’ data. The data see the best selling products with the most returns and feedback from clients for selling the clothes and shine out the recommendations of products and items. The data analytics’ use can go with an improvement of clients’ experience.

Machine learning similarly has the ability to learn the already having data and offers the needed foundation for the machine to provide teaching itself. Machine learning processes are developed for assuming the events and conditions.

Machine learning even sees the patterns and learns it from them for adapting the nature and behavior for incidents that occurred naturally. Thus, during the mining of data it is useful for the source of information to pull it. Though data scientists can use the mining of data for automatic and particular data types and its parameters. It does not apply and learns the knowledge of its interaction of humans.

Recognition of patterns

Gathering the data is the only part of the challenge where the other part is making the sense of this. The proper tools and software are required for analyzing and interpreting the large amounts of information and data scientists to gather and determine the patterns for acting upon. Else, the data will be unused unless the data scientist can dedicate their time to looking out for these complex, often suitable and seems to be the patterns randomly on their own. And anyone who is familiar with data analytics and data science knows this can be time-consuming tasks.

Businesses can even use this data for shaping the forecast of sales and identify the products their clients really wish to purchase. For instance, it gathers the sales points for its warehouse of data. Shops can even see this information and utilize it for determining the patterns of purchasing and guide their assumptions of inventory and future’s processes.


It is very true to the mining of data which shows the patterns via analysis of sequence and classifications. Thus, machine learning takes this topic as the further steps while using the similar processes of data mining which utilizes the automation learning. As the protection of malware becomes a high issue, machine learning can look out for the recognition of patterns in the method of data in systems or access to the clouds.

The difference between data mining and machine learning is also discussed in detail with its foundation and recognition process to better understand how machine learning is different from data mining.

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