Data Mining: Definition, Process, Benefits, and Application



What is data mining?


Data mining is the process of finding anomalies, patterns, or correlations in large data sets to predict outcomes. The basis of data mining itself is related to disciplines such as statistics, AI, machine learning, and database technology. Data mining is also known by other names such as data/pattern analysis, knowledge discovery, knowledge extraction, and information harvesting.


Basically the existence of data mining is needed considering the increasing amount of information in the technology era such as business transaction data, scientific data, images, videos and other data. With this amount of data, a system is needed that is able to extract the essence of all available information and make summaries to help make better decisions.


Process in data mining


The data mining process consists of several steps, namely:


  1. Business understanding

The first step in the data mining process is to define project goals and find out how data mining can help you achieve those goals. In this stage a plan must be developed such as determining the schedule, action, and division of roles.


  1. Data understanding

The next step is to collect data from all existing data sources. At this stage, data visualization tools are used to explore the properties in the data.


  1. Data preparation

In this stage the data that has been collected will go through data cleaning and data transformation. Data cleaning or data cleaning is carried out on inconsistent or incomplete data. While data transformation is done by changing the data to make it useful in data mining.


In data transformation, several things can be done such as smoothing (removing noise from data), data aggregation, generalization, normalization, and attribute construction. The data preparation process can usually take up the most time of the entire process. That’s why at the data preparation stage, usually a DBMS or database management system will be used to increase the speed of the data mining process.


  1. Data modeling

At this stage a mathematical model is used to find patterns in the data. Modeling techniques will be adapted to the business objectives at the outset. In addition, a new scenario will be created to test the quality and validity and then run it on the prepared dataset. The results must be assessed to ascertain whether the model can meet the data mining objectives.


  1. Evaluation


The data findings will then be evaluated and compared against business objectives to determine if they can be used across the organization.


  1. Deployment


In this final stage, the data mining findings will be shared with various business operating platforms within the company.



Benefits of data mining


By doing data mining, companies can get many benefits. Some of the benefits of data mining are:

  • Easy decision making. Companies can continue to analyze and automate routine decisions without delays due to human judgment.
  • Make accurate predictions for planning. Data mining helps the planning stage and provides precise information to make predictions based on past trends and current conditions.
  • Cost reduction. Data mining allows companies to use the allocation of funds more efficiently because the automation of decision making can reduce costs.
  • Gain insights about customers. Companies can find out the characteristics between customers so that they can design strategies that can improve customer experience appropriately.


Examples of the application of data mining


The use of data mining itself is quite extensive. Usually data mining techniques are used to build machine learning models that can support modern artificial intelligence applications such as search engine algorithms or recommendation systems.


In addition, data mining is often used in various industries and disciplines such as:



Data mining is used by multimedia and telecommunications companies to understand the volume of customer data, predict their behavior and offer targeted or relevant campaigns.



Another application of data mining is in the insurance industry. Insurance companies generally use data mining techniques to detect fraud, identify risk factors in filing claims, analyze customers, to find ways to offer competitive products to their existing customer base.



Data mining is used such as to adjust supply plans and demand forecasts, quality assurance, predict production assets and anticipate maintenance.



Used to help companies optimize marketing campaigns, improve customer relationships and forecast sales.



Data mining helps educators in accessing student data, predicts achievement levels and provides insight into which students or groups of students need extra attention



Data mining helps financial services companies to get a better view of market risk, detect fraud, manage regulatory compliance and to get optimal returns from marketing investments.



The importance of data mining for data scientists


In their work, data scientists are often assigned to analyze data that can help businesses. In order to do this, you must also be able to communicate complex results and observations so that they can be understood and acted upon from a business perspective. Therefore, it will be very useful if a data scientist can have the ability in the field of data mining.

Data mining will assist data scientists in compiling raw data, formulating it and recognizing various patterns through mathematical algorithms and communication to unlock useful insights.


Data mining method


In the process, data mining has planning and methodologies that harmonize implementation ideas from beginning to end. This method can be summarized in two main methods of data mining which are as follows,


Data retrieval

This data collection process is carried out in stages through raw data which is then selected and processed into information or a common thread of data. The stages of the process include several things such as:

  • Data cleansing, in the early stages of data mining, raw data is cleaned of errors or incompleteness and data inconsistencies.
  • Data integration, this stage is carried out by integrating data that has been cleaned and combined if data similarities occur.
  • Selection, this stage is carried out before data mining to select and select data that has been cleaned to look for its relevance to the analysis process or general database.
  • Data transformation, this stage is carried out by placing the relevant data into data mining procedures with a data aggression process
  • Data mining, the main stage in the data retrieval process is data mining, which is where identification is carried out with measurements or general terms that have been agreed to take certain patterns.
  • Knowledge presentation, this final stage is done visually to make it easier for users to understand the results of data mining.


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