How is Data Mining Different from Machine Learning?

How about we take a closer look at data mining and machine learning so we know how to catch their different ends?

How is Data Mining Different from Machine Learning?


We live in a data-driven information-rich digital era where businesses witness new technical terms and concepts from time to time. Now that more businesses are adapting to Artificial Intelligence and Machine Learning, there are tons of possibilities for Big Data and Data Analytics to show wonders. Data is a crucial tool; however the more data available, the longer it takes for organizations to gain insights. 

This is why businesses need Data mining. Data mining opens various opportunities for business since it has descriptive and predictive powers. Data mining in business intelligence helps in future road mapping, product development, and business processes. Additionally, both data mining and machine learning fall under the general heading of data science.

There is a lot of overlap when it comes to applications of data mining and machine learning which is why enterprises use them interchangeably. It’s intriguing how with similar functions both technologies work with data differently. How about we take a closer look at data mining and machine learning so we know how to catch their different ends? 


What is Data Mining?


Data mining extracts critical information from enormous data. People use data mining techniques to discover new, accurate, and functional patterns in the data to find the meaning and information.

Let's break down the data mining process to see how experts implement the functions and tackle the data challenges. 

  1. Understand the business objective: This includes defining the objective, term goals and current situation of business. 
  2. Understand the data achieved: Here we figure out what kind of data is needed to solve the problems. The required data is then collected from the proper sources.
  3. Prepare the data: Here the data quality problems like duplicacy, data corruption, and missing data are resolved. Then data is presented in a suitable format. 
  4. Model the data: This means applying algorithms to the data in order to identify patterns, insights and more valuable information. 
  5. Evaluate the data: Here businesses comply with whether the insights achieved to fulfill the required goals and objectives or not. 
  6. Deploy the solution: If the data insights align with required business goals, decisions are made. 

As the process suggests, data mining is one efficient way to resolve complicated data challenges. Now, how else is data mining beneficial to your business? 


Benefits of Data Mining in Business


It’s crucial to ascertain as many benefits from data as possible in today’s competitive business world. Here’s a broad range of benefits data mining offers to a business. 

  1. Data mining is a cost-efficient solution as compared to traditional data approaches such as BI tools and software systems. 
  2. Businesses make profitable production and operational adjustments using data mining approaches. 
  3. Data mining complies with both new and legacy systems which mean enterprises of any scale and size can implement its approach. 
  4. Businesses use the data mining information to access risk models, detect fraud and improve product safety. 
  5. Businesses also quickly initiate automated trends and behavior and make informed decisions based on rich data. 


Data Mining and Machine Learning


If you look forward to using data mining to its full potential, it's essential to use “the right tool for the right job”. While we are talking about different tools and techniques for diverse data mining functionalities, one such tool is Machine learning.  

Machine Learning is one of the tools of data mining that includes discovering algorithms to improve the courtesy of the experience derived from data. Data mining uses techniques crafted by Machine learning for predicting results while Machine learning is a capability of a system to learn from a minded data set. 

The evolution of machine learning throws light on some of the trickiest problems that took time to do manually. Today, Machine learning and data mining have made it much easier to create complex algorithms for large data processing and providing useful outcomes. 


Data Mining Vs. Machine Learning


Data mining uses two components (database and machine learning) for data management and data analysis techniques. It helps extract valuable data that can provide excellent insights into a product or service. 

However, machine learning only uses algorithms and possesses a self-learning capability to change rules per scenario to find the solution. 

Another contrasting difference lies in the human effort, as data mining requires constant human intervention but machine learning only requires humans to define the algorithm. As machine learning is an automated process, it will work on its own to produce accurate results compared to data mining. 

Data mining is limited to how data is organized and collected and acts as a means to extract relevant insights from complex datasets. Machine Learning identifies the correlations between all relevant data points to deliver accurate conclusions and ultimately shape the model’s behavior. 

For example, CRM systems implement machine learning procedures to enhance their relationship intelligence to understand customers better. It can analyze past actions to boost conversions and improve customer satisfaction scores. Here’s a small comparison table to help you differentiate between data mining and machine learning better. 

The Basis for Comparison Data Mining Machine Learning
History It was introduced in 1930 and was referred to as knowledge discovery in databases. It was first introduced in 1950 for Samuel’s checker-playing program.
Origin It originated from traditional databases that contain unstructured data. It originated from existing data and algorithms. 
Meaning It helps to extract information from a large dataset. It introduces a new algorithm from data.
Responsibility It is used to get the rules from the existing data. It teaches the computer to learn and understand the given rules.
Nature It is more of a manual process and requires human interference.  It is an automated process and does not require human effort once the design is implemented. 
Implementation Users can develop models to use the data mining techniques. The user can use it in the decision tree, neural networks, and specific areas of artificial intelligence.
Application & Abstraction It is used in cluster analysis, and data is abstracted from the data warehouse. It reads machines and is used in web search, credit scoring, spam filtering, fraud detection, and computer design.
Scope We can apply it in a limited area. We can use it in a vast area.


Final Thoughts


Data mining procedures help predict the result from historical data or find a new solution from the existing data. Machine learning overcomes the problems of data mining which is helping it to grow in a much faster way. Also, machine learning is more accurate and less error-prone, making it capable of making its own decision and resolving issues. 

However, it is essential to keep the data mining process as it will define the problem of a particular business. Data mining and machine learning are required to drive a business and work together in a better way.

Sudeep Srivastava is the CEO of Appinventiv, is someone who has established himself as the perfect blend of optimism and calculated risks, a trait that has embossed itself in every work process of Appinventiv. Having built a brand that is known to tap the unexplored ideas in the mobile industry, he spends his time exploring ways to take Appinventiv to the point where technology blends with lives.