Undergraduate in Electrical Engineering at Federal Technological University of Paraná (UTFPR). Developed a Startup project at UTFPR's Incubator of Innovations - IUT through a selection process by the Technological Hotel. Has advanced certification of Machine Learning analysis using Python language by IGTI and has White Belt training in Lean Six Sigma. Specialist in content optimization in the area of Research and Development at Think Lean Six Sigma.

Understand what Data Mining is, what this practice is for, and how it can help in the statistical control of your company!

What is Data Mining? Find out!

<p>One of the most remarkable skills the human brain possesses is to recognize patterns and analyze data. It is exactly this capacity that researchers try to replicate on computers, and for that there is Data Mining.</p><p>In the area of ​​Computer Science, these surveys began after World War II and obtained technological results capable of transforming the world in which we live.</p><p>Data Mining (DM) is one of these innovative technologies.</p><p>In this article you will read about:</p><ul><li>What is Data Mining?</li><li>What is the difference between Data Mining, Big Data and Data Warehouse?</li><li>Data Mining application steps</li><li>Main Data Mining techniques</li><li>Main applications of Data Mining</li></ul><h2><strong>What is Data Mining?</strong></h2><p>Also known as Data Mining, Data Mining is an algorithm used within a large database to recognize patterns and rules that can aid in making a decision.</p><p>That is, with the accumulation of data and information generated today, much useful knowledge can end up getting lost amidst this. It is necessary to analyze these data and look for patterns, that is, to look for hidden treasures. That&#39;s why we use Data Mining.</p><p>This process is composed of three areas of knowledge: Classical Statistics,<a target="_blank" rel="noopener"  href="https://www.thinkleansixsigma.com/article/artificial-intelligence"> Artificial Intelligence</a> and Machine Learning.</p><p>Classical Statistics is the origin of the main methods used in Mining, such as analysis of variance and normal distribution. Artificial Intelligence, however, seeks to analyze data similar to the human brain.</p><p>Machine Learning is the combination of the two concepts previously mentioned. Through this technique it is possible to induce computers to make decisions, with the aid of algorithms that recognize statistical patterns, and to become able to make predictions.</p><p>Regarding its origin, it is possible to affirm that Data Mining became known in the 1990s. At this time, traditional techniques were no longer effective in storing all the data of an organization.</p><p>In this context, DM has become one of the most promising tools on the market. In addition to providing a million dollar economy to companies when collecting data, it was able to capture significant information.</p><h2><strong>What is the difference between Big Data, Data Warehouse and Data Mining?</strong></h2><p>Although they are related concepts, it is not correct to state that Data Mining, Big Data and Data Warehouse have the same meaning.</p><p>Big Data is characterized by the vast amount of random data produced all around the world. Data Mining is the recognition of patterns within this data. The Data Warehouse is the information bank in which all of these results are stored.</p><p>It&#39;s time for you to start your training in one of the most valued methodologies on the market!</p><p>With our <a target="_blank" rel="nofollow"  href="https://thinkleansixsigma.thinkific.com/courses/lean-six-sigma-black-belt-upgrade">Green Belt training in Lean Six Sigma</a>, you will develop fundamental skills and knowledge for your professional growth!</p><p>Our teaching methodology has content such as videos, exercises, complementary readings and personal assistance, so you can take the next step towards Green Belt professional recognition in Lean Six Sigma.</p><p>Don&#39;t waste time and reach the next level in your professional career!</p><p><a target="_blank" rel="nofollow" href="https://thinkleansixsigma.thinkific.com/courses/lean-six-sigma-black-belt-upgrade">          <Image  quality="95"  src="https://images.prismic.io/voitto-blog/97fe760e-630f-45d0-9d4a-8f9cf76c75ef_Green+Belt+-+Bot%C3%A3o+Banner.png"          width="1026"          height="206" alt="Green Belt in Lean Six Sigma" title="Green Belt in Lean Six Sigma"/></a></p><h2><strong>Data Mining Steps </strong></h2><p>To design a case study using Data Mining, you need to follow a few steps. Regardless of the programming language chosen, the data scientist should also pay attention to the following steps:</p><h3><strong>Problem Definition Problem</strong></h3><p>definition is the first step of the Data Mining process. In this phase the objective is to understand the problem and to establish what the objective is to achieve with the mining process.</p><h3><strong>Data Exploitation</strong></h3><p>It is in the exploration of data that the basic statistical tools begin to be used. This is also the stage at which experts collect, describe, and explore data. In addition, the quality of all data is also tested.</p><h3><strong>Data</strong></h3><p>preparation is a process that depends on the origin of the data. Thus, depending on the state in which the raw data is found, it is necessary to prepare them by methods of filtering, combining and filling empty values.</p><h3><strong>Modeling</strong></h3><p>This step has a direct relationship with the objective of each Mining process, since it is necessary to choose a modeling technique, within Data Mining, that guarantees the solution of the proposed problem.</p><h3><strong>Evaluation</strong></h3><p>The evaluation is the most critical phase of the process, since it requires the participation of a group of people specialized in Data Mining and the business under analysis to evaluate if Data Mining has achieved the desired result.</p><h3><strong>Implementation</strong></h3><p>Implementation is the final step of the Data Mining project. It is at this stage that you import the results obtained into the databases or to other types of directories.</p><h2><strong>Data Mining Techniques</strong></h2><p>The main Data Mining techniques consist in a very extensive area, so there is not just one way to find patterns within a large volume of data.</p><p>Below you will be able to check which are the main techniques used when transforming data into information:</p><h3><strong>Association rule</strong></h3><p>Association rule discovery is one of the most used techniques for discovering knowledge in Data Mining, since it is possible to extract a simple solution of complex cases.</p><p>This technique consists of analyzing the relationship between the items of a certain data set and finding trends and / or patterns that can be used to understand the behavior of these data.</p><p>A very popular and elucidative example of the rules of associations is that of the supermarket. According to this explanation, if a person goes to the supermarket to buy milk and bread he will also buy butter.</p><p>Thus, this technique is very common in marketing campaigns and inventory control of shopping centers, since the purchase of an A product may imply the sale of the product B.</p><h3><strong>Artificial Neural Networks </strong></h3><p>Artificial neural networks (RNA) present a mathematical model based in the central nervous system. This type of algorithm seeks to solve problems by simulating the behavior and functions of a neuron.</p><p>Its operation occurs through dozens or even hundreds of processing units, which are interconnected by communication channels.</p><p>In this way, the entrances are similar to dendrites and simulate an area of ​​stimulus capture. The output of data is compared to the neurons and the contact between these elements forms the synapse.</p><p>In some Neural Networks the output of one neuron can also become an input signal to another. Thus, RNAs are capable of generating several types of distinct structures.</p><h3><strong>Decision</strong></h3><p>Decision<strong></strong>trees function as one<a target="_blank" rel="noopener"  href="https://www.thinkleansixsigma.com/article/flowchart"> flowchart</a>, but they are shaped like a tree. Through this model, it is possible for the user to make decisions based on numerous possibilities of choice.</p><p>These possibilities are automatically tested and work as follows:</p><p>The node represents data or problems and each branch has a cluster of solutions based on costs, probabilities, and benefits.</p><h2><strong>Main Applications of Data Mining </strong></h2><p>Data Mining currently has thousands of applications around the world, so this concept is more present in your day to day than you can imagine.</p><p>How about taking a look at the examples below and finding out how this artifice is part of your routine?</p><ul><li>Customer acquisition: identification of the profile of potential buyers of a particular product;</li><li>Supermarket: allocation of products on the shelves according to the consumption profile of their customers;</li><li>Security: detection of criminal and terrorist activities;</li><li>Telemarketing: capture of data of possible clients;</li><li>Human Resources (HR): analysis of the competencies of a curriculum;</li><li>Bank: identification of standards that can assist in the management of the relationship with the client.</li></ul><h2><strong>Learn More!</strong></h2><p>As you can see, Data Mining is increasingly present in our daily lives and has been able to revolutionize the business world.</p><p>But these benefits do not stop there. With the advancement of technologies and<a target="_blank" rel="nofollow"  href="https://www.voitto.com.br/blog/artigo/industria-4-0"> Industry 4.0</a> you will still hear a lot about topics like this.</p><p>So do not forget to follow our blog, there you find content that is released daily, on the technological, business and student world.</p><p>Did you like our text? Leave yours feedback for us, we want to know your opinion!</p><p>Are you tired of missing out on incredible career opportunities because you don&#39;t know about the main Excel tools?</p><p>So you are in the right place! 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Find out what is Data Mining and what it is used for! Check it out!

Discover the importance of Data Mining in today's market, as well as how the most efficient collection techniques work!