Is Data Mining Part of Machine Learning?

Machine learning and data mining – these two are buzzing terms in 2020. Because we’re all stuck at home, many people have been researching the latest and greatest that the world of technology has to bring to the table, and these two terms pop up with almost every search.

Machine learning enables artificial intelligence, one of the most popular technologies in this day and age. Data mining is enabling everything from data streaming and ML to Big Data, both of which are as popular as ever.

Many people are a bit confused when it comes to data mining and its relationship with machine learning. In this article, we’ll discover this relationship, define both of these buzzing technologies, and explain how their symbiosis can help shape the world of tomorrow, today! 

What Is Data Mining?

Data mining is the process in which an algorithm discovers patterns and anomalies within a datasheet or database. It’s used for extracting usable data from massive databases, which contain raw data. Raw data is all of the data collected by crawlers and data harvesters and is usually full of redundancies, situational data, and false data.

Only usable data is extracted from these data sheets through data mining, which can then be processed and used for further analysis. Data mining is an essential process that enables things such as big data, advanced data science, IoT, and in this case, machine learning. 

Data mining is done by data mining software, which can range widely in application and complexity. Usually, data mining software is available commercially for broader applications. Still, if the datasheet is highly unique or vastly complex, custom-built data mining software might be required to do the job correctly.

Data mining isn’t the fastest process, and it can take quite a long time for a piece of software to discover all of the anomalies and patterns in one datasheet. That depends on the complexity, amount, and sophistication of the data and the sophistication of the data mining software. 

What Is Machine Learning?

Machine learning is one of the most important technologies of this century. It enables the advancement of artificial intelligence, which is reshaping the world around us. In theory, machine learning is the study of computer algorithms that advance in sophistication through experience.

Machine learning is the key that allows computers the ability to learn from vast sets of data, allowing them to become more sophisticated automatically, without the need of a third party. In layman’s terms, machine learning is the programming that enables computers to learn from experience. 

This advanced technology allows for machines’ automatic development, allowing them to undertake new tasks and gain unique insight without any prior programming. 

Machine learning, at the current moment, is mostly used for AI and works by extracting and pushing data from huge datasheets. 

These data sheets need to undergo sophisticated refinement before they’re suitable for machine learning, as teaching the AI the wrong things could hinder the whole process. 

The Similarities and Differences Between the Two

There are many similarities between machine learning and data mining, explaining why people tend to confuse these two terms. 

While data mining works to extract and refine data from massive data centers and data sheets, machine learning uses data to allow the AI to learn from experience. Both of these technologies deal with data, making the data itself a center point of their similarities. 

While similar, the two have drastically different applications. Data mining works to extract data, refine it, and prepare it for further implementation – machine learning utilizes data for AI applications. 

There are many similar applications of both technologies that still vary, such as:

  • Enhancing data accuracy
  • Streamlining CRM solutions
  • Data analytics
  • Forecasting and predictions
  • Pattern recognition

These technologies are used to automate and streamline many processes that go on in the background of AI, data science, and up-and-coming technologies such as Big Data. 

Do Data Mining and Machine Learning Exist in Symbiosis? 

Yes. Since machine learning requires enormous data sets to operate appropriately, data mining is the preferred tool for data refinement and extraction. 

Data comes in through data harvesting software, which is nowhere near sophisticated enough to harvest viable and usable data. Data mining is used to aid these huge data sheets and rid them of any unusable data by extracting only the essential parts that can be used to select things, one of which is machine learning.

The data extracted through data mining has to undergo further refinement, which consists of further refinement, sectioning, and data preparation, making it ultimately usable for machine learning applications. 

Just like machine learning can use data extracted by data mining, data mining itself can use machine learning to improve its internal operations. Data mining is usually done by software, and augmenting this software’s performance through the introduction of ML solutions can refine its operations and improve its yield. 

Final Thoughts on Data Mining and Machine Learning

Machine learning and data mining are two different things, but that doesn’t mean they don’t intersect at points. Both are extremely useful for selecting applications, one of the most interesting of which is artificial intelligence. 

Artificial intelligence relies heavily on distributed messaging and queuing systems such as Pandio, an Apache Pulsar based, AI orientated cloud data delivery system.  

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