What is Data Mining? : The other name of Data mining is Data/knowledge discovery. Data mining is the process of searching small data suitable for its needs from very large data stores. Traditional statistics, artificial intelligence and computer graphics are used in the process of searching small data. Those who have information related to this need not be told that "Data mining is a useful technique using which companies extract important information from a large group of data."
By using data mining, hidden patterns and useful data are discovered and then decisions are made based on these patterns and data, and using the process of data mining organizations solve problems in business. Various types of data mining tools are also used to analyze the data in the data mining process, which are very powerful.
What is Data Mining?
Data mining is a process in which a large amount of data is analyzed to find some patterns and some useful information. These are usually done in databases which store the data in a structured format. In this, by "mining" large amounts of data, some hidden information is also obtained from it, which can be used for some other work.
Data mining is used to extract data from very large data sets and we can say that the data is filtered and classified. In data mining this is done so that we can study the data and sort the data. Data mining tools help us to understand the future trends.
Data mining techniques are used in a lot of research, mathematics, cybernetics, genetics and marketing. It is widely used by large companies. Big companies make full use of it and increase their profits. It is also widely used in bioinformatics to operate equipment. It also predicts the behavior of the customers and also increases the efficiency of the work. If we learn to use it properly then we can do business very well with it.
Applications of Data Mining
You must have come to know from the above explanation that data mining is used in many sectors. For more information read about the application of data mining below:
Healthcare - It is used to find out about the patient's illness. It gives information about such hospitals where patients can be treated with less money and less time.
Market - Customer behavior is traced through data mining. It is being seen in this that if the customer has bought something similar, then what other items will he buy with it. Through data mining, it is also found out what kind of item the customer is searching on the internet.
Education - Using data mining to predict student's outcome. It also explains how and what to teach a student.
Fraud Detection – There are a lot of frauds happening these days. Due to frauds the money of millions of people over the world is wasted. Data mining helps to avoid this.
Applications
1. In the field of mobile phones:- Data mining is used by mobile phone companies to get information of their customers. Like which customer bought the mobile from which shop or website and which phone bought etc.
2. In the field of banking :- In the field of banking, data mining is used to identify frauds in credit cards, identify loyal customers and obtain other information.
3. In the field of retail: - In the field of retail, data mining is used to get information about consumers, sales, which customers buy the most, salmon transportation, consumption and service information.
4. In the field of insurance :- Data mining is used in the field of insurance to predict which consumer will take which insurance.
5. In the field of medicine:- Data mining is used in the field of medicine to analyze and predict the behavior of the patient. Understanding the patient's condition, which medicine should be given to him and for how long the patient should be given, etc. things are predicted.
6. In the field of science:- Data mining is used in meteorology to predict weather conditions and to discover new information in geology, in astronomy and in biology.
7. In the field of law:- Data mining is used in the field of law to predict the behavior of criminals, in crime patterns, to locate the location etc. so that criminal cases can be solved easily.
8. In the field of defence:- Data mining is used in the field of defence to predict which weapon will end up in the army base and when it will be supplied. And it can be guessed from what things will be needed.
Examples of Data Mining
A credit card company uses data mining to understand the shopping habits of its members. While analyzing the purchases of cardholders, the company can study their shopping habits, it can also know how people from different places make more purchases.
This information can be very important in providing certain specific promotions to those individuals. Their buying patterns can also be understood from data mining data, regardless of the country or province they are from. This type of data is very beneficial for new companies who want to advertise or start a new business.
Online services such as Google and Facebook mine vast amounts of data so that they can provide targeted content and advertisements to users. Google also analyzes similar search queries, putting such popular searches into specific fields in its autocomplete list (these are suggestions that appear as you type).
Facebook also gets information about many different topics by mining user activity data, while it targets ads based on that information.
Data mining is mainly used for marketing purposes, while it has many other uses as well. For example, healthcare companies can use this data mining to find links about certain genes and diseases. As mentioned above.
The meteorological department can also mine the data and find out the weather patterns and with the help of this can predict the future meteorological events.
Traffic management can also mine these automotive data and predict what kind of traffic levels are going to happen in the future and make plans for highways and roads accordingly.
Need for Data Mining
Data mining requires two main things - a lot of data and a lot of computing power. The more organized the data, the easier it is to precisely mine it and obtain useful information.
That's why it is very important for any organization that wants to engage in data mining, they have to be proactive, choosing what kind of data to log and how to store it.
When it comes to data mining, supercomputers and computing clusters are needed to process petabyte amounts of data.
Types of Data Mining Analysis
As can be seen in the above image that there are two types of data mining analysis, which are as follows:-
1. Predictive Data Mining Analysis : It predicts future events. It is of four types.
➤ Classification Analysis
➤ Regression Analysis
➤ Time Series Analysis
➤ Prediction Analysis
2. Descriptive Data Mining Analysis : It is used to convert data into useful information. It also has four types:-
➤ Association Rules Analysis
➤ Sequence Discovery Analysis
➤ Clustering Analysis
➤ Summarization Analysis
Here I discuss some special types in short:
Classification Analysis : In this, categories are created according to the type of data mined, that is, a separate category is created for each type of data so that the data can be used immediately when required. In addition, mathematical algorithms are also used for data classification.
Prediction Analysis : This technique works on the basis of historical buying and selling, i.e. on the basis of past data, the forward strategy is drawn and the latest trend is also taken into account, and future sales and profits are also taken into account. . is the predicate.
Association Rules Analysis : By this technology, information related to customer's interest in a product and customer's buying habit is collected, and accordingly companies make their strategy, and show products according to their interest to different customers so that sales can be increased.
Sequence Discovery Analysis : In this, a single pattern i.e. form of data is traced from historical data, and placed in a sequential manner. It is a type of order list through which a type of purchase and product interest is expressed by the customer at different times.
Clustering Analysis : In this technique of data mining, similar and different data is first understood, and then divided into different groups according to the type of data. Each group is completely different from the other group, these different types of groups are called groups.
Summarization Analysis : In this, the data set is broken down into smaller subsets, and the condition is applied, and then the data is collected based on the decision. That is, at the time of purchase of a product in front of the customer, questions or conditions are formed and various answers related to them are also recorded, whose Summarization Analysis works.
Data Mining Techniques
Data mining works in four phases, in the first phase - the data source - it handles the difficulties in a way, it is from the database to the news wire. Data gathering in the second stage (Data gathering) – In this we collect the data and sample the data. Modal in the third step - The user creates a modal test and then views it. Deploying the modal in the fourth step - in this you can take any action based on the result.
The clustering parameter finds documents and then correctly places them. Clustering groups data in a way that organizes the data into sets and some that are common, it also organizes them according to them in the same way.
In this, users can perform clustering in many ways, which are useful in clustering modelling. Fostering parameters search for patterns within data mining and predict future activities which we also call predictive analysis.
Data mining techniques are used in a lot of research, mathematics, cybernetics, genetics and marketing. It is widely used by large companies. Big companies make full use of it and increase their profits. It is also widely used in bioinformatics to operate equipment. It also predicts user behavior and increases the efficiency of the task. If we learn to use it properly then we can do business very well with it.
Web mining is also a type of data mining used in CRM (Customer Relationship Management). It also comes in handy for evaluating user behavior and how the website is doing.
Other data mining techniques include network knowledge to classify multitasking patterns, applying data mining algorithms, mining large databases, complex data types, and tools for data mining in machine learning. We use a lot of techniques to create.
Use Of Data Mining
In data mining, the exact raw data is checked, after which the necessary information is gathered from that raw data, which is used for analysis.
If we talk about the use of data mining technology in a business, then data mining collects information related to its customers, such as customers' preferences, needs, demands, etc. Based on this, marketing strategies of the business are created, and sales increase.
For example, when you search for a product on the Internet, a record of the information you search is established, which is saved as data on servers on the Internet, and stored whenever you access the Internet again. When you use it, you start seeing more and more new information related to the last searched product. So all this is done by data mining, in which information is collected by the data mining process by filtering the same raw data and it is used in business development.
Advantage of Data Mining
➤ Through the technique of data mining, the company derives information from the knowledge base.
➤ Through this organizations improve their production and operations.
➤ As compared to other statistical data applications data mining is cost effective i.e. it saves cost.
➤ Through this, decisions can be taken easily.
➤ It is very easy to implement in new systems.
➤ large data is analyzed in less time because its speed is very fast.
➤ It gets profitable customers easily, which makes it easier to sell the product and also improves the relationship with the customer.
Disadvantage of Data Mining
➤ Its biggest disadvantage is that there is no security and privacy of data in it. It collects all the data like social media messages, photos etc. It would not be wrong to say that people's privacy is violated.
➤ Mostly collected data are incomplete through the data mining.
➤ It also collects irrelevant (useless) data.
Characteristics of Data Mining
➤ It predicts the future. predicts future events can be done by it.
➤ It focuses on large datasets and databases.
➤ In this, the prediction of patterns is automated and it is based on behavioral analysis.
➤ his creates useful information.
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Last Word
Friends, I hope that from this post you have got all information about the Data Mining, If you want to ask any kind of question related to data mining, then you can ask by commenting. if you like the given information, then definitely share it with your friends too. Thank you.
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