Difference Between Machine Learning And Data Mining

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Yes, friends, it happened. It’s time to admit it to ourselves. Artificial intelligence is actively communicating almost everywhere. We are not only talking about the work process but also the household chores. Despite the skepticism of futuristic and movie-inspired AI, it’s time to take a breath and believe it. In fact, most mundane tasks can easily be crossed off your to-do list. In particular, there are two main technologies to consider: data mining and machine learning. Data mining and machine learning are mainly focused on helping companies develop decision-making tools without human intervention. In addition, the decisions made may become the basis for action in one way or another. Don’t worry, you won’t lose control. You can set the limits of technological freedom. And this “opportunity” is conditional if the program learns your habits and develops a decision algorithm that can predict what you do, point you to areas of development that may be interesting or useful directions. Hundreds of problems are solved in seconds, thanks to the ability to perform deep and comprehensive analysis of data that is often stored in a chaotic and unstructured way. Sounds too good, doesn’t it? Let’s take a look at how each technology works, what the differences and similarities are between data mining and machine learning, and which solution is best for your business. What is data mining? The development of methods of recording and storing data has led to a rapid increase in the amount of information collected and analyzed. The amount of data is so overwhelming that it is impossible to analyze it alone, although the need for such analysis is obvious, because this “raw” data contains knowledge that can be used to make decisions. To perform data analysis automatically, Data Mining is used. Data mining is the process of finding interpretations of previously unknown, useful and accessible knowledge necessary for decision-making in various fields of human activity in “raw” data. “. Data Mining is one of the steps of Finding Knowledge in Databases. The information found in the process of applying the data mining method must be arbitrary and not known in advance. For example, average marketing does not fit this term. Knowledge must define new relationships between properties, predict the value of certain attributes based on others, etc. Discovered knowledge must be applied to new data with a certain level of reliability. Its usefulness is that this knowledge can bring various benefits to its application. The knowledge must be in a non-mathematical form that the user can understand. For example, the logical construct “if… then…” is easily understood by humans. Also, these rules can be used in various DBMS as SQL queries. If the extracted knowledge is not transparent to the user, there must be a post-processing method to convert it into an interpretable form. Algorithms used in Data Mining are computationally intensive. Previously, this limited the widespread application of data mining, but the current increase in the performance of modern processors has eliminated the severity of this problem. Today, you can perform quality analysis on hundreds of thousands and millions of files. The task solved with the data mining classification method is the assignment of objects (observations, events) to one of the previously known classes. Recession, including forecasting issues. Create continuous dependencies from input variables. A grouping is a collection of objects (observations, events) based on data (properties) that define the importance of objects. Objects in one cluster must be “similar” and different from objects belonging to other clusters. The more similar objects there are in a cluster and the more different the clusters are, the more accurate the clustering will be. Association: Identifies patterns in related activities. An example of such a pattern is the rule showing that event Y follows event X. Such a rule is called associative. This problem was first proposed to find a common way of shopping in supermarkets. Therefore, it is sometimes also called market basket analysis. Sequential model: establishes a pattern between events in relation to time, that is, the discovery of the dependence that if event X occurs, event Y will occur after a certain time. Deviation Analysis: Identify the most unusual patterns. Business analysis problems are formulated in different ways, but the solution to most of these problems comes down to one or more data mining problems or a combination of them. For example, risk assessment is a solution to a regression or classification problem; market segments are groups; It is a relational principle to stimulate demand. In fact, data mining is the key to gathering solutions to most real-world business problems. To perform the above tasks, data mining methods and algorithms are used. Due to the fact that data mining has evolved at the intersection of disciplines such as statistics, information theory, machine learning, and data theory, it is natural but most algorithms and data mining methods have been developed based on various methods of these disciplines. . . For example, the k-means clustering method was simply borrowed from statistics. The following data mining methods have become very popular: neural networks, decision trees, clustering algorithms, including scalable ones, algorithms to identify relationships between events, etc. What is machine learning? Machine learning (ML) is an approach to artificial intelligence, a set of algorithms used to create machines that learn from experience. As training, the machine processes large amounts of incoming data and finds patterns in it. Machine learning is a branch of AI, an algorithm that allows computers to draw conclusions from data without following well-defined rules. This means that machines can see patterns in many complex problems (that the human brain can’t solve), and find more precise answers. The answer is a true prediction. Applications and goals of machine learning The goal of machine learning is to partially or completely automate the solving of complex analytical problems. Therefore, first of all, machine learning is designed to make the most accurate predictions based on input data so that business owners, marketers and employees can make better decisions in their work. As a result of training, the machine can predict the result, remember it, repeat it if necessary, and choose the best one from many options. Today, machine learning covers a wide range of applications, from banks, restaurants, gas stations to robots in manufacturing. New challenges emerging almost every day lead to the emergence of new directions in machine learning. How to build quality machine learning Machine learning is built on three pillars: Data: The basic information we often ask our customers. This includes a sample of all the data that needs to be trained to work with the system; Hint: this part of the work is done in close cooperation with the customer. We identify the key business requirements and together decide what features and properties the system should follow as a result of the training; Algorithm: the choice of the method to solve the given business problem. We are resolving this issue without customer intervention through the efforts of our staff. Data The more data we enter into the system, the better and more accurate it will work. The data itself depends directly on the task the machine is dealing with. For example, to teach email to filter spam from important emails, an example is needed. And the bigger the sample, the better. Therefore, the system learns to understand certain words – “Buy”, “Additional income”, “Earn at home”, “Money”, “Credit”, “Potential growth” – as a sign of spam and send letters so to split folders. . . The initial data for other jobs will be different. In order to inform the buyer about products that may be of interest to him, the account holder’s purchase history is required. To predict market price changes, we need price history. The most difficult and, at the same time, the most important part of the work is the collection of these data. There are two methods of data collection: manual. The manual method is slower but accurate. automatically. Auto is faster, but leaves more room for error. A good sample of data is very valuable because it is ultimately responsible for the accuracy of the estimates obtained. It is very important not to limit the collection of data to the imagination of humans, ​​​​​​but to provide as much information as possible, because machines can see interests and relationships that people would not be aware of. Indicator (attribute, metric, attribute, attribute) For example, if the

Difference Between Machine Learning And Data Mining

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