Data Mining vs Machine Learning: The Data Revolution

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Terms like “machine learning” and “data mining” are frequently used in the context of technology and data analysis. Despite their similar names, they have different functions in the field of data analytics. Let’s examine the differences between Data Mining vs Machine Learning and how each helps to make sense of big datasets.

What is Data Mining?

The process of extracting information or hitherto unidentified patterns from massive data sets is known as data mining. Consequently, we ‘Mine for specific data’ from the big data set, as the term implies. The science of data mining, also known as the knowledge discovery process, is used to ascertain the characteristics of the datasets. 1989 saw the creation of the phrase “Knowledge Discovery in Databases” (KDD) by Gregory Piatetsky-Shapiro. In the database community, “data mining” first appeared in 1990.

Large volumes of information are gathered from data warehouses or intricate datasets like time series, spatial, etc., and are then extracted to reveal intriguing correlations and patterns among the data points. The data mining algorithm’s output is frequently used as input for machine learning algorithms.

What is Machine Learning?

By enabling computers to learn and write their code, machine learning aims to make them behave and make decisions more like people. Throughout the process, the machine learning approach is automated and improved based on the machine’s experiences. The goal of machine learning, a data mining technique, is to create algorithms that improve the usability of experiences derived from data. A system’s purpose is to extract meaning from a specific set of data, while data mining forecasts results using techniques developed by machine learning.

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Difference Between Data Mining vs Machine Learning

  • Their Age: To begin, data mining originated before machine learning approximately two decades ago, initially known as knowledge discovery in databases (KDD), a term still used in certain contexts today. Machine learning, on the other hand, emerged in the 1950s, debuting in a checker-playing program.
  • Their Purpose: The primary objective of data mining is to uncover patterns and rules within large datasets, whereas machine learning focuses on teaching computers how to learn and make decisions based on given parameters. In simpler terms, data mining is a method used to analyze data to predict specific outcomes based on accumulated information. Conversely, machine learning trains systems to perform intricate tasks using data and experience to enhance intelligence over time.
  • What They Use: Data mining typically operates on extensive datasets, often referred to as Big Data, to generate forecasts and insights for businesses and organizations. In contrast, machine learning utilizes algorithms rather than raw data to enable systems to learn and adapt.
  • The Human Factor: This is where there is a big difference. Since data mining is ultimately designed for human use, it depends on human intervention. On the other hand, the entire purpose of machine learning is its ability to learn on its own without the help of humans or their actions. Data mining is just impossible without a real person using and interacting with it. On the other hand, human interaction with machine learning is essentially restricted to the initial algorithm setup. After that, it’s just a matter of leaving it alone—a “set it and forget it” approach. Data mining is done by humans while machine learning allows the systems to take care of themselves.
  • The Ability to Grow: This one is simple: machine learning is all about adapting and learning, whereas data mining is incapable of doing either. Data mining is static and adheres to predetermined rules, whereas machine learning adapts the algorithms when appropriate conditions arise. Users’ input determines how intelligent data mining is, and machine learning implies that those computers are becoming more intelligent.
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Advantages of Data Mining

  • Through data mining, businesses, organizations, and governments can obtain trustworthy information.
  • Data mining uncovers fraud and issues that traditional data analysis methods might overlook.
  • Through data mining, variations and patterns in user behavior can be found. 

Advantages  of Machine Learning

  • Large data sets can be reviewed by machine learning, which can spot patterns and trends that people might overlook.
  • In uncertain or changing environments, machine learning algorithms are skilled at handling multidimensional and multivariate data.
  • Machine learning algorithms can automate specific procedures, thereby reducing labor costs and freeing up organizations to focus on other activities that add value.

Tools used in Machine Learning

  • OpenNN: a neural network library that is open-source and intended for use in research, education, and industry.
  • PyTorch: A popular research and development tool for deep learning, this open-source framework is renowned for its adaptability and dynamic computation graph.
  • Vertex AI: The integrated platform from Google Cloud for creating, honing, and implementing machine learning models.
  • Microsoft Azure Machine Learning: a cloud-based platform that makes use of Microsoft Azure to develop, train, and implement machine learning models.
  • IBM Watson: IBM offers a range of AI and machine learning services, including computer vision and natural language processing tools.
  • Google TensorFlow: a popular deep learning and neural network framework created by Google that is available as an open-source project.
  • Amazon Machine Learning: a division of Amazon Web Services (AWS) offering machine learning services and tools in the cloud.

Techniques Used in Data Mining

  • Classification: This technique allows for the collection of essential and relevant data as well as metadata details. This data mining procedure makes it easier to categorize data into multiple groups.
  • Clustering: Data mining techniques such as clustering analysis identify comparable data. This method identifies variations and commonalities in the data.
  • Regression: Regression analysis is a data mining technique used to discover and evaluate relationships between elements by adding another component. 
  • Outer: This type of data mining technique involves identifying data points in a data set that deviate from a typical trend or predicted behavior.
  • Sequential Pattern: Sequential pattern is a data mining method that examines sequential data to detect recurring trends. Finding interesting segments among a group of sequences is what it entails. The length, frequency of occurrence, and other factors all contribute to a sequence’s significance.
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FAQ of Data Mining vs Machine Learning

Q1. Which ten data mining algorithms are there?

Ans.

  • C4.5 algorithm
  • CART algorithm
  • K-mean algorithm
  • Apriori algorithm 
  • KNN algorithm 
  • Adaboost algorithm
  • PageRank algorithm 
  • Support Vector machines
  • Naive Bayes algorithm 
  • Expectation-maximization algorithm

Q2. Which is superior, machine learning or data mining?

Ans. Compared to data mining, machine learning can yield more accurate and faster results because it is an automated process.

Q3. What is the optimal language for machine learning?

Ans. While they offer efficient speed, languages like R, C++, or Java are difficult to learn. While more difficult to use, some advanced languages—like Python and JavaScript—execute more slowly. Python is regarded as a necessary language for data analytics and machine learning.

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I'm a tech enthusiast and content writer at TechDyer.com. With a passion for simplifying complex tech concepts, delivers engaging content to readers. Follow for insightful updates on the latest in technology.
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