Data is the word that is more common these days. Data is referred to as the smallest and simplest unit of content. All signs, numbers, and statistics that are collected by researchers or the system and do not have additional explanations are considered data. Another definition that can be described for data is “the science of extracting patterns, knowledge, and analysis of raw data from a set of data is called data mining.”
Some consider data and information to be the same. So that the data has raw and uninterpreted content, in other words, it is considered broad content information and data is a subset of information. These two topics have completely different meanings, and we will explain the difference between data and information in detail below. All these ambiguities are collected in the question of what is data mining, which we have answered in the rest of this article.
(Data mining extracting useful information from raw and unintelligible data sets.)
The difference between data and information
In the previous articles, we mentioned the difference between data and information so that there is no ambiguity in this matter. This science has created conditions that can be used to collect and extract hidden data and use it in important decisions by using new technologies such as artificial intelligence, machine learning, etc.
One thing about data is that it is raw and untouched, which can be useless. A group of irregular and meaningless numbers is considered as data, but the results obtained from the processing of this set are the same as information, and the information can be used again as input data of the system. So, according to these materials, we can conclude that, in general, raw inputs and information are called data.
If you still do not understand the difference between data and information and you did not find the answer to the question of what is data mining, we will explain it with a simple example so that you can easily get acquainted with data mining:
Grades of students of a class, data and processing results on these grades, average grades, increase or decrease of course grades, graphs, etc. are considered as information. In other words, the data is a collection of statistics and raw data that have no meaning, and the information is considered the advanced version of the data.
After analyzing the data with a certain purpose, information is obtained. Information is the opposite of data, which cannot be obtained from different perspectives due to its small concept. He pointed out the capabilities of information that anyone with any opinion and viewpoint can have a different perception of it.
Applications of data mining
We mentioned before that this extracts useful information from raw and unintelligible data sets that have different parts. So far, you have almost understood what data mining is and how it differs from information, in the following we will discuss some of the applications of data mining so that you can know more about Kavira data:
- Identification of autosamplers
- Prediction of results
- Discovery of patterns in data
- Generate actionable information
- Focus on large data sets and databases
- Obtaining practical information
We think you have understood what data mining is and what its uses are. In general, data mining helps us to remove incomprehensible and useless data from our collection. In addition, it provides us with effective and useful information and speeds up the decision-making process.
In addition to the cases mentioned above, data mining is widely used in banking, retail, and social networks. Or another interesting and scientific example of data mining can be mentioned using YouTube. As you know, YouTube is a space for sharing all kinds of videos. You must know that a few seconds of advertisements are played before the desired video is played. If you pay attention to the content of these advertisements, surely one of these advertisements will be according to your needs and desires, or if you log into your user account in Gol, it will display advertisements for you that are exactly related to your interest.
Have you ever wondered how Google knows what you are looking for? The answer to the question is that Google, through its search engine and your search history, can access the sites that you visit the most and learn about your interests and tastes in various topics. All these are raw and unintelligible data that are not very useful in our opinion. But with the help of updated data mining algorithms, Google can obtain good information from the data it has from you. For this reason, this search giant can easily find out your interests and use this personal information in its advertisements. Unbelievably, this work has helped and will help increase Google’s income. This is one of the simplest applications of data mining that we explained to you.
Modeling in data mining
There are different dimensions in the data mining process, which include understanding the goals of the project and setting up and making the necessary changes. This process is done in three steps:
- Model learning
- Model evaluation
- Using the model
These divisions are considered regular and functional divisions in data mining. The learning model is used when an algorithm is executed on a group or class of data that has a classifier value, or by examining the data, a new algorithm is identified. This classifier is tested with an independent set of data that has the desired properties. The higher the level of equality of the classified parameters of the model, the higher the level of accuracy should be. As a result, if the model is accurate enough, it can be used to classify organizations and data sets with unrelated characteristics.
Problems in data mining!
Considering the importance of data mining in today’s business and also the achievements of data mining, it has created problems and challenges in this field.
Data mining problems include:
- A large amount of data in the input
- Security and privacy issues
- The difficulty of presenting some intuitive concepts of hidden phenomena in the data Difficulty understanding the complexities in the data
- Lack of complete confidence in the output information
- The need to choose the right analysis method to obtain useful results
Therefore, the raw data of collections and organizations may contain effective and valuable information from different parts of users’ lives, maintaining and maintaining data security is not an easy task. In addition to this, users’ privacy should be prevented as much as possible.
On the other hand, raw data is not easily obtained for data mining and information acquisition, or if it is obtained, it cannot be easily separated and categorized from redundant and useless data. Therefore, cleaning scattered data is one of the difficulties and problems.
The results obtained from data mining must have a certain validity and efficiency, and the selection of data analysis methods must be done more carefully, all of which are challenges of data mining.
Conclusion
With this account, the period of ill-considered decisions is over and now these data play an essential role in important economic, social and political issues. It is necessary to use data mining in business management because using different data mining methods in business will save it from failure and not benefiting from the methods of this science will prevent you from having different opportunities. he does.