An Insight Into the Differences Between Data Mining and Machine Learning

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

Data mining was introduced two decades before machine learning and was called Knowledge Discovery in Database (KDD). Introduced in the 1930s, the goal of data mining is to identify relationships and links between the attributes in a dataset to predict outcomes. While machine learning introduced in the 1950s includes gaining knowledge from past data and using the knowledge to make future predictions.

Data mining is a cross-disciplinary field that utilizes machine learning along with other techniques for discovering the properties of a dataset. The latter is a subset of data science that focuses on designing algorithms that can learn from data and make predictions accordingly. Thus, data mining uses machine learning but not vice versa.

2. The Purpose

Data mining helps extract rules from huge quantities of data, while machine learning helps a computer learn and understand the parameters. In other words, data mining is just a method of researching to arrive at a specific outcome based on the collected data. Machine learning trains a system to carry out complex tasks and utilizes harvested experiences and data to become smarter.

3. Inputs Used

Data mining depends on large stores of data for making forecasts for businesses and other organizations. Machine learning uses algorithms instead of raw data.

4. Manual v/s Automatic

Data mining depends on human intervention and the process is carried out for the use of people. On the other hand, machine learning exists to teach itself and not rely on human actions or influence. Data mining cannot work unless a human being interacts with it. Human contact in machine learning is simply limited to setting up initial algorithms and is a ‘set it and forget it’ process.

5. Learning Ability

Data mining cannot adapt or learn, something that is the chief model of machine learning. Data mining is static and follows pre-set rules, while machine learning adjusts its algorithms as the desired circumstances manifest themselves. The outputs of data mining rely on the intelligence of the users who enter the parameters; machine learning implies that the computers become smarter with every set of data entered.

6. Accuracy

Data mining and machine learning, both are used for improving the accuracy of collected data. However, the evaluation of data mining is primarily limited to the way data is arranged and accumulated. Data mining is a way to extract important insights from complex datasets and boost the predictive abilities of machine learning models and algorithms.

While data mining can miss out on multiple relationships and connections between the data at hand, machine learning does not. It can recognize the connections between important data points and deliver highly accurate conclusions, thereby shaping the behavior of the model.

7. Ways of Use

The specialties of both processes are distinctly defined. Data mining is extensively used in the retail industry for understanding the buying habits of customers and helping businesses devise better sales strategies. Social media plays a vital role in data mining by helping collect information from the profiles, keywords, shares, and queries of users. This assists advertisers in putting together appropriate promotions. The financial sector uses data mining for researching opportunities for investment and determining the likelihood of a startup’s success. Collecting such information assists investors in deciding if they want to invest money in new projects.

Machine learning is used by companies for purposes like online customer service, self-driving cars, email spam interception, credit card fraud detection, personalized marketing, and business intelligence. Big companies like Google, Twitter, Facebook, Salesforce, Pinterest, and Yelp, all depend on machine learning.

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.