Data Mining Definition, Functions, Methods, and Applications

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Data mining definition as a process in collecting important information on a big data. In its application the data mining search process often uses statistical methods, mathematics, and the use of artificial intelligence.

Alternative names for data mining are Knowledge Discovery in Database (KDD), business intelligence, and others. This process includes data correction, data selection, pattern evaluation, and so on.

By using various techniques, people can use this information to increase their income. It can also be done to cut costs, improve relationships, to reduce risk.

Why Need Data Mining?

Data mining definition as a tool for data analysis that has a variety of functions. There are two main functions in data mining itself, namely descriptive functions and predictive functions.

• Descriptive function in data mining definition as a function in understanding the data that people observe. Simply put, it is possible to find out the characteristics of a data. Can also find a pattern from a data.

• The data mining prediction function is defined as a function of how the process finds a pattern in the data. Patterns can be known from an existing variable. This pattern is useful for predicting hidden variables.

The importance of using data mining because it is based on its function. This function is quite easy and profitable for anyone who needs accurate predictions. Mainly to make important things better.

And there are also other important data mining functions such as: characterization, association, discrimination, clustering, outliers, classification, and trend analysis, etc.

Performing data mining methods plays an important role in accelerating the decision-making process. So it can be good for assessing the possible results of data collection.

Launching from business information to stay afloat in the era of low touch economy. The use of data mining is quite important to help business development.

Examples of Data Mining and Data Mining Applications

Data mining itself is defined as an activity and not a program or algorithm. In its application it is useful as a technique with many disciplines. For example, such as statistics, machine learning, and so on.

The application process in revealing patterns that people do not know we can call definition data mining. It is an important tool in converting data into information.

So, in what fields can people apply data mining? Here are some applications of data mining.

1. Market analysis and management. Such as looking at buying patterns over time, customer profiles, identifying needs, and much more.

2. Company analysis and risk management, such as financial planning, resources, and so on. It can also be used for analysis by finding competitor data for comparison.

3. Telecommunications serves as a tool to view many incoming transactions. And can also determine which transactions require manual handling.

4. Finance as a recorder or detector of financial transaction processes, especially suspicious ones. For example, the process of money laundering.

5. Insurance can identify health services that are less necessary. So that it can help insurance participants pay for insurance more effectively.

6. The NBA has used sports in the statistical analysis of NBA games. Such as the number of fouls, assists, and shots blocked.

7. Astronomy has helped the Jet Propulsion Laboratory find 22 quasars using data mining.

8. Internet Web Surf-Aid, IBM Surf-Aid uses data mining to record web page access. Useful to see consumer behavior and interest in seeing marketing effectiveness.

Data Mining Method

Data mining is defined as a means to help solve various problems through data. There are several data mining methods that people can use, namely as follows.

1. Classification

Is a fairly common method in data mining. Various business problems involve various classification methods such as risk management, churn analysis, and so on.

The purpose of this classification method is an action to provide action to a group. Especially in each situation that needs to be divided into several groups.

2. Grouping

The clustering method aims to identify various groups in a case. It is based on data and its various supporting components.

For example grouping the younger generation with low income, and the easy generation with middle income. Then group the various supporting information to perform data analysis.

3. Association

Is a shopping cart analysis, where people need to analyze the sales transaction table. Moreover, identify various products by customers simultaneously.

Then the existing similarities can be used for identification. So that you can determine what kind of product is suitable. In addition, it can also look for rules such as what causes the product to be more salable.

4. Regression

This method is similar to the classification method. But there is something that makes it different. That is not being able to search for a pattern globally.

So regression serves to find a pattern by determining a numeric value. Regression is very useful in solving business problems.

Examples include estimating distribution methods, seasons for estimating wind speed based on temperature and so on.

5. Forecasting

Becoming a data development method that is quite important, such as answering the value of a large company's stock. Or it could be for the analysis of the number of sales of certain products in the next month.

6. Sequence Analysis

A method that is useful as an analytical tool to look for patterns of an event. For example a DNA that has various sequences that are very closely related.

Simply put, there is someone who originally bought a computer at a store. Then he bought speakers at the store. And one day he also bought a webcam at the shop.

7. Deviation Analysis

Useful for finding cases and acting as usual. For its use, it is quite broad, one of which is detecting credit card abuse.

As for other uses, namely disturbances in computer networks, production analysis errors, and so on.

Data Mining Stages

A series of processes that can be divided into various stages is a data mining definition. There are several stages of data mining that people can do, which are as follows.

1. Data Cleaning

Obtaining data from an experimental analysis is of course necessary for sorting. The reason is that not all data that people get is valid and useful. At this stage it affects the performance of the data mining system.

Because the data can reduce the number and completeness. Irrelevant data is better to be discarded. And only take a few that are relevant to make it more effective and efficient.

2. Data Integration

Identifying data by synergizing the various supporting components. For example, such as product type, name, customer number, and others.

At this stage, care needs to be taken to be appropriate and precise. Because if it is wrong, it can lead to distorted results. For example, if the data comes from the type of product, the supporting components must also be appropriate.

3. Data Transformation

At this stage also determine the quality of the results of the existing data later. Because, there are several characteristics and data mining techniques that people need to transform.

4. Application of Data Mining Techniques

Some data mining is not yet perfectly applied. However, to apply it can be in various fields as mentioned above.

5. Pattern Evaluation

The results of data mining are in the form of distinctive and predictable patterns. So if the results of the pattern do not match the hypothesis, you can take various other alternatives.

6. Presentation of Patterns in Generating Action

In the last stage, namely the process of how to formulate a decision from the results of the existing analysis. Sometimes it can even involve some people who don't understand data mining.

Can apply data mining into various business processes. Today's digital business is enough to help various company development processes. Do not be left behind in terms of business digitization development.