Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery!. Are you interested in learning more about data mining?

If so, spend a few minutes reading this article to gain a thorough understanding of data mining. And get ready to dive into the field of data mining.

The following is the article’s structure:

  1. What is the purpose of data mining?
  2. What is Data Mining?
  3. Process of Data Mining
  4. What types of data can you mine?
  5. Data mining technologies are used.
  6. Data mining in practice

1. Why Data Mining?

“We are living in an information era,” as the saying goes, but the facts show that we are living in the data age. Various sources generate massive amounts of data every day.

Computer networks, search engines, social media, medical departments, science, business, and other sources are among them.

Businesses, such as Amazon, generate enormous amounts of data every day. They process millions of transactions per day, as well as client information, shopping trends, and much more.

Similarly, a large amount of data is created every day in the medical department. Patient records, medical records, and so on are examples. Because of the large number of searches, the search engine creates a lot of data every day.

The user generates a large quantity of data every day on Social Media, either by submitting an image or by sharing content.

Without a question, this is why we live in the data age. And this massive amount of information is quite valuable.

So, Data Mining comes into play in order to extract usable information from these massive amounts of data.

Data mining extracts useful information from massive amounts of data. As a result, it is critical in the data industry.

2. Data Mining Definition

Data mining can be defined as “knowledge mining from data.” To put it another way, data mining is “knowledge mining.” Data mining is used to extract meaningful information or knowledge from a data tank.

Data mining accomplishes the same goal as “gold mining.” Gold is harvested from rock or sand in “Gold Mining.”

Similarly, data is mined from a massive volume of trash data in “Data Mining.” Only meaningful data is mined in data mining.

Data mining is also known as “knowledge mining,” “pattern mining,” “knowledge extraction,” and other terms. Data mining is also known as “knowledge finding.”

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3. Data Mining Process:-

The steps in the data mining process are as follows:

  1. Data cleansing.
  2. Integration of data
  3. Data compression
  4. Data transformation
  5. Data analysis
  6. Evaluation of patterns
  7. Knowledge is presented.

1. Data cleansing

We collect data that is both incomplete and noisy. The very first step in data mining is to clean the data. We remove missing values and noise throughout the data cleaning process.

Many fields in the data are blank due to missing values. As a result, we fill in the blanks in the data cleansing process.

2. Integration of data

We merge data from multiple sources in data integration. Because information does not come from a single source. We gather information from several sources and then merge it into one.

As a result, when integrating data, you must ensure that no redundant or inconsistent data is introduced. You must perform correlation analysis and entity identification when integrating data.

3. Data Reduction

The amount of data we acquire is enormous. As a result, data mining on large data sets takes a long time. That is why, in order to reduce data size, we execute data reduction.

The Wavelet Transform and Principal Component Analysis are two common data reduction techniques.

4. Data Transformation

We turn data into a suitable format for data mining during data transformation. Smoothing, aggregation, normalization, and other data transformation procedures are employed.

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5. Information mining

Data mining is done after data transformation. The data mining technique uncovers a number of intriguing patterns.

6. Pattern Analysis

Data mining uncovers numerous patterns, but not all of them are fascinating. The interestingness of patterns is discovered at this step.

The pattern that is the most fascinating is maintained, while the others are deleted. Some “interestingness measurements” detect this interestingness.

7. Knowledge Presentation

We represent this knowledge to the user once the patterns or knowledge have been mined. The data mining process comes to a close with this stage.

4. What types of data can you mine?

Data mining can be done on any type of data, although the most fundamental type of data is-

  1. Database Information
  2. Warehouses of data
  3. Transactional Information
  4. Multimedia Information
  5. Geographical information
  6. Web Data.

5. Data mining Technologies

Various technologies are utilized in data mining. Here, We’ll describe some of the technologies that data mining employs.

  1. Machine Learning
  2. Pattern Recognition
  3. Statistics
  4. Data warehouse
  5. Retrieval of information

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6. Data mining in practice

Data Mining’s Uses- Data mining is useful in practically every field. We are going to teach you about several data mining application areas where data mining is crucial.

Data mining is used in business.

The business industry revolves around data mining. We believe that today’s commercial industry is incomplete without Data Mining. Data mining is used by several business areas depending on their requirements.

Many consumers, for example, shop on Amazon on a daily basis. As a result, Amazon collects these users’ data, does mining, and discovers distinct purchase trends.

Consider purchasing a laptop and headphones on Amazon. So, the next time you go to Amazon, you’ll notice another laptop and headphones under the Recommendation section. This is a Data Mining exercise.

A supermarket is another corporate example of data mining. So, after performing data mining on grocery data, it was discovered that when someone buys bread, they also buy jam. The grocery manager can improve the sale of the jam by combining bread and jam using this strategy.

Data mining in search engines

Data mining is used extensively in search engines. When you use a search engine like Google to look for something, the search engine saves your information.

The search engine makes recommendations based on your search history. For example, if you search for mobile phone types and pricing. As a result, you will receive mobilephone related recommendations.

Following are some interesting books to read:

  1. Data Analytics Made Accessible: 2022 edition
  2. Data Smart: Using Data Science to Transform Information into Insight
  3. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

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