Data Mining Vs Machine Learning - The Key Difference

Table of Contents

What is Data Mining?

Data mining is a part of business analytics that involves delving into a vast dataset to discover hidden patterns, relationships, and anomalies that we might not have been aware of before. It allows us to uncover completely new insights that were not on our radar – the unknown unknowns, so to speak.

For instance, imagine a company with a large amount of data on customer churn. By using data mining algorithms, they can uncover unexpected patterns and associations in the data that indicate potential customer churn in the future. This is why data mining is commonly used in retail to identify trends and patterns that can help businesses make informed decisions.

Also read: Data Cleaning: How to Clean, Components, Advantages, Benefits

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What is Machine Learning?

Machine learning is a branch of artificial intelligence (AI) that allows computers to analyze extensive datasets and “learn” from the patterns they discover. Once trained, the computer can use this knowledge to make predictions about new data without requiring ongoing human interaction.

Machine learning is like teaching computers to learn from information, much like humans do. It involves interpreting data and understanding our successes and failures to improve our predictions. This process is beneficial for making accurate predictions about various outcomes.

For example, when Netflix suggests that you might enjoy watching “Ozark” based on users’ similar profiles’ viewing preferences, it’s an example of machine learning in action. Another instance is real-time fraud detection on credit card transactions, where machine learning algorithms can swiftly identify potential fraudulent activities.

Also read: Supervised Learning in Machine Learning

Key Differences Between Data Mining and Machine Learning

Introduction and Techniques:

Data mining and machine learning are two components used in data analysis. Data mining relies on the database for data management techniques, while machine learning uses algorithms for data analysis.

Data Utilization:

Data mining utilizes extensive data to extract valuable insights and predict future outcomes. For instance, a marketing company may use last year’s data to forecast sales. On the other hand, machine learning relies more on algorithms and doesn’t depend heavily on vast amounts of data. Transportation companies like OLA and Uber use machine learning techniques to calculate the Estimated Time of Arrival (ETA) for rides based on algorithms.

Learning Approach:

Data mining follows predefined guidelines and can provide specific answers to particular problems. It lacks self-learning capabilities. In contrast, machine learning algorithms are self-defined and can adapt their rules based on the situation. They find solutions to specific problems in their unique way.

Human Involvement:

Data mining requires human involvement throughout the process. It cannot function without human input. However, machine learning is automated, and human effort is only required during the initial algorithm definition. Once implemented, machine learning can work independently, making it a more sustainable solution than data mining.

Precision:

Since machine learning is an automated process, its results are more precise than data mining.

Tools and Techniques:

Data mining involves using a combination of tools such as the database, data warehouse server, data mining engine, and pattern assessment techniques to obtain useful information. On the other hand, machine learning relies on neural networks, predictive models, and automated algorithms to make decisions.

Data mining and machine learning are both valuable approaches in data analysis, but they differ in their reliance on data, human involvement, learning approach, and precision of results. Machine learning is a more automated and adaptable technique, making it increasingly popular in various industries.

S.No.	Data Mining	Machine Learning
1.	Extracts useful information from large amounts of data	Introduces algorithms from data and past experience
2.	Used to understand the data flow	Teaches the computer to learn and understand from data flow
3.	Utilizes huge databases with unstructured data	Utilizes existing data and algorithms
4.	Develops models using data mining technique	Uses machine learning algorithms in decision trees, neural networks, and other AI areas
5.	Requires more human interference	No human effort is required after the design
6.	Used in cluster analysis	Used in web search, spam filter, fraud detection, and computer design
7.	Abstracts from the data warehouse	Reads from machine
8.	More focused on research using machine learning,	Self-learns, and training systems to perform intelligent tasks
9.	Applied in limited areas,	It can be used in a vast range of applications
10.	Uncover hidden patterns and insights.	Make accurate predictions or decisions based on data
11.	Exploratory and descriptive	Predictive and prescriptive
12.	Uses historical data	Uses historical and real-time data
13.	Identifies patterns, relationships, and trends.	Provides predictions, classifications, and recommendations
14.	Involves clustering, association rule mining, and outlier detection.	Involves regression, classification, clustering, and deep learning
15.	Involves data cleaning, transformation, and integration	Involves data cleaning, transformation, and feature engineering
16.	Often requires strong domain knowledge.	Domain knowledge is helpful but not always necessary
17.	Applied in various fields like business, healthcare, and social science.	Primarily used in areas where prediction or decision-making is crucial, such as finance, manufacturing, and cybersecurity.

Also read: Complete Guide About Machine Learning Hackathons | PW Skills

FAQs

What is the major difference between data mining and data science?

Data mining involves extracting meaningful information, patterns, and trends from vast databases. In contrast, data science encompasses the process of obtaining valuable insights from both structured and unstructured data through the utilization of various techniques and tools. Data mining is a specific method within the broader field of data science.

Can you explain the distinction between data mining and deep learning?

Data mining involves uncovering concealed patterns and rules from existing data using association and correlation rules. On the other hand, deep learning is utilized for more intricate tasks, such as voice recognition.

Can you explain the three Vs. of data mining?

The three Vs. of data mining are volume, velocity, and variety. They are crucial in comprehending the measurement of big data and how it differs from traditional data. You can learn more about the 3Vs of Big Data at Big Data LDN, the top data conference, and exhibition in the UK for your entire data team.

What is the difference between data, information, and knowledge?

Data is raw information. Information is data that has been processed and organized. Knowledge is the understanding gained from information.

what are the four primary data mining techniques?

Data mining employs four techniques for generating descriptive and predictive capabilities: regression, association rule discovery, classification, and clustering.