An Insight Into the Differences Between Data Mining and Machine Learning

Data Mining Services

An Insight Into the Differences Between Data Mining and Machine Learning

Introduction

The rapid growth of the digital world has resulted in the introduction of multiple new technical phrases and terms. When it comes to data, terms like data science, data analytics, big data, and machine learning are used in many official meetings, boardroom discussions, and conferences. This often results in confusion among people on the actual meaning of these terms. Many of these terms might even sound similar but each term is distinct from the other. Here we’ll discuss the difference between two widely used terms— data mining and machine learning.

Data Mining

Data mining is pivotal when it comes to businesses like marketing, retail, banking, or communication. It is a process that helps extract useful information from a large amount of data. Data mining is used for discovering accurate, new, and beneficial patterns in the data, and searching for relevant information and meaning for the organization that requires it. It is a tool that is used by humans.

The chief goal of data mining is to discover information or facts that were previously not known or ignored using complex mathematical algorithms. It leverages the strength of various pattern recognition techniques from machine learning to extract knowledge and unknown patterns from huge data sets.

Machine Learning

A subset of artificial intelligence (AI), machine learning provides computers the ability to learn on their own without being programmed and improve with experience. Machine learning applications learn from previous transactions and computations and utilize ‘pattern recognition’ for producing reliable results.

Machine learning removes the human element from learning to make machines smarter and more intelligent. The technology finds applications in everyday life be it fraud detection, product recommendations, traffic predictions, or your personal assistants like Siri and Alexa.

Are There Any Similarities Between Data Mining and Machine Learning?

Data mining and machine learning both make use of data for solving complex problems and the two terms are often erroneously used interchangeably. This does not come as a surprise because machine learning is at times used for conducting practical data mining. As data collected from data mining can be used for teaching machines, the differences between the two concepts tend to blur. Also, both processes use the same complex algorithms to discover data patterns. Nevertheless, the desired results delivered from the two processes differ from each other.

Understanding The Differences Between Data Mining and Machine Learning

While there are some similarities between the two terms, there are a considerable number of differences as well. Let’s understand the differences between data mining and machine learning:

1. The Time of Origin

2. The Purpose

3. Inputs Used

4. Manual v/s Automatic

5. Learning Ability

6. Accuracy

7. Ways of Use

Conclusion

With more and more businesses wanting to become more predictive and the amount of data increasing, machine learning and data mining are here to stay. The technologies impact business decisions via data patterns and help organizations in scaling up their decision-making and analytical abilities.

Damco is one of the leading companies in web data mining adept at helping you solve complex business problems via its standard mining practices. Our experts have in-depth expertise in data mining across common industry verticals like real estate, marketing, finance, etc.

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