Almost All Industries, Sciences, And Engineering Disciplines Have To Understand The Nature Of Large, Complex, And Information-Rich Databases To Do Their Jobs Better. 

Predictive And Descriptive Data Mining: Business and customer information are recognized as strategic assets in the business world. The ability to extract hidden useful knowledge in this data and act on this knowledge has become increasingly important in today’s competitive world.

The process of applying computer-based processes and patterns, which include new techniques and tools to discover knowledge from data, is called data mining. Simply put, the process of applying a computer-based method, which involves new techniques for extracting knowledge from raw data, is called data mining.

Data mining is an iterative process involving discovering knowledge through automated or manual methods performed in a cycle to extract reliable information eventually.

Data mining is advantageous in exploratory analysis scenarios where there is no pre-determined concept of what is to be achieved in results. For example, with the onset of a pervasive crisis, what interactions may industries such as oil and gas face?

Data mining is the search for new, valuable, and hidden information in large data volumes that require the joint effort and cooperation of humans and computers. For this reason, the best results are obtained when experts can accurately describe problems and goals, and computers can search for information based on exploratory patterns.

Predictive data mining

Predictive data mining is a predictive approach that uses some variables or fields within a data set to predict unknown present or future developments or identify variables with information value. Here, the term prediction refers to the output of an algorithm that predicts specific probabilities based on receiving new datasets and applying them after training on previous sets.

For example, whether a company’s stock value may fall or rise in the future. Accordingly, predictive data mining refers to constructing a system model that arises from the set of data it has received.

In predictive data mining, the goal is to build a model that can be cited as an executable code and used in processes such as classification, forecasting, estimation, and similar tasks.

Descriptive data mining

The descriptive data mining process focuses on finding descriptive patterns of data that humans can interpret. The goal here is to build a new model based on nontrivial information based on available data sets. In descriptive data mining, the goal is to gain accurate knowledge about the system being analyzed using the patterns and relationships that govern big data.

Predictive and descriptive models in specific data mining applications are significantly different from each other. In both cases, to take advantage of the index of descriptive and predictive approaches, the data mining techniques mentioned in future articles should be used.

Steps of data mining

To be able to implement an effective predictive or descriptive model, processes need to be performed. In general, to perform preliminary data mining, the following steps must be performed carefully based on the selected model.

(Classification) Classification: One of the most important data mining processes that assign elements in a set to target categories or classes. The purpose of classification is to accurately predict the purpose class to which the data must belong. For example, a classification model can identify and label loan applicants based on low, medium, or high credit risk.

More precisely, who may pay the installments on time, some with a few days delay and some not be able to pay the installments.

(Regression) Regression:

Regression is another important function of data mining that predicts numbers. For example, a regression model can predict a home’s value based on location, the number of rooms, floor area, and other factors. A regression process begins with a set of data in which the target values ​​are known. In regression, a data element is mapped to a real-value predictor variable.

Clustering: One of the common processes in descriptive data mining in which an expert seeks to identify a limited set of categories or clusters to describe data.

Summarization:

An additional descriptive task that includes methods for identifying a concise description of a set of data.

Dependency Modeling: Finding a local model that describes significant dependencies between variables or values ​​of a property in a data set or part of a data set.

Change and deviation detection refers to detecting and identifying the most important changes applied to the data set.

The introductory classifications and definitions we have provided are intended only to acquaint readers with the complexity of the concepts associated with data mining and the capabilities that data mining offers. In future tutorials, we will try to describe complex and large datasets using graphical diagrams more comprehensively.

Success in accurately implementing a data mining process depends largely on knowledge, creativity, and the designer’s amount of time training the model. In fact, data mining is like solving a riddle.

 The individual pieces of the puzzle do not have a complex structure per see.

However, when combined as a single set, they describe large and comprehensive systems. When you first experience inexperience in such a scenario and take the first steps, which involves examining the puzzle, you are likely to feel defeated because you do not know exactly what to do, where to start and go. Sequence What steps should be taken?

However, once you know how to work with the puzzle pieces, you will find that it is not as difficult as it sounds. The same rule applies to the world of data mining.

In the beginning, data mining process designers probably did not know much about data sources. If resources were available, there would be no need to do data mining because everything was readily available, and even the process could be done manually.

 Separately, the data looks simple, complete, and explicable.

However, in the state of consensus, they seem like a jigsaw puzzle that is a little scary and difficult to understand. Therefore, in a data mining project, the analyst and designer must have accurate knowledge and a creative mind and view problems from other angles.

Data mining is one of the fastest-growing areas of information technology. That is why experts have predicted that data mining will rapidly enter other fields and industries in the next few years. One of the biggest strengths of data mining is that it offers unique solutions and techniques to solve a set of problems.

Given that data mining is a process that is routinely performed on large data sets such as data warehouses and data databases, jobs such as online retailers, factory production lines, telecommunications companies, the healthcare industry, and financial institutions. And transportation is interested in this field.