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Top 10 Data Mining Applications in Real World

With Big Data becoming an indispensable part of the industry, data mining as a process has become indispensable to Big Data. Its broad range of techniques can be used to increase revenues, reduce risks, cut costs, improve customer relationships, and many more. Data mining applications are vast, but we will briefly be looking at a few of them.

We will go through a list of Data Mining applications that are big and very popular right now or have future potential. We will first answer the question, ‘what is data mining?’ followed by covering the types of data, benefits of data mining, and its applications.

Over the past few years, significant upgrades and advances in speed and processing power have allowed us to move beyond manual and time-consuming methods to quick and efficient automated data analysis. The more complex the data, the more insights there are to uncover through data mining.

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What is Data Mining?

A core component of the knowledge discovery process or KDP, data mining is the process of extracting valuable information that makes sense from a large chunk of data. It helps explore and identify user trends and patterns in datasets. Data mining essentially has an interdisciplinary approach that involves the use of statistics, database technology, AI, and Machine Learning methods. Data mining makes use of algorithms for the extraction of patterns in datasets.

To learn more about Data Mining, go through our blog on Data Mining and Statistics.

Types of Data for Data Mining

The following are the types of data that undergo data mining:

  • Object-oriented and object-relational databases
  • Data warehouses
  • Relational databases
  • Text databases
  • Transactional and spatial databases
  • Text mining
  • Web mining
  • Information repositories
  • Heterogeneous and legacy databases
  • Multimedia and streaming databases
  • Advanced DB

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Benefits of Data Mining

  • Data mining is an effective technique for companies to obtain knowledge-based insights.
  • The automated prediction of trends and behaviors and the discovery of hidden patterns are possible with data mining.
  • Data mining helps organizations optimize their operations and production.
  • Data mining enables better decision-making.
  • Compared to other statistical data applications, data mining is a better, faster, and more cost-effective solution.
  • Data mining is flexible and can be employed in new platforms as well.

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Practical Applications of Data Mining

Data mining has primarily seen applications in companies with a strong consumer focus that requires a good drill-down into their customer preferences, product positioning, customer satisfaction, impact on sales, transactional data, corporate profits, etc. Let’s take a look at the top applications and trends in data mining:

Data Mining Applications in Healthcare

Data mining holds great potential in the healthcare sector. Data and analytics can be used to identify best practices as well as provide cost-effective solutions. The data mining approach includes multi-dimensional databases, statistics, Machine Learning, data visualization, and soft computing that can have massive applications in the industry. It can help predict the volume of patients in every category, improve processes to ensure that patients receive appropriate care without delays or setbacks, detect fraud and abuse for insurance purposes, and many more.

Application of Data Mining in Banking and Finance

With the advent of digitization, the banking sector is handling and managing enormous amounts of data and transaction information. Data mining applications in banking can easily be the appropriate solution with its capability of identifying patterns, casualties, market risks, and other correlations that are crucial for managers to be aware of. Despite the volumes of data, results can be generated almost instantly for the managers to make sense of without much effort.

Bank officials and employees will find use all the information to improve and optimize segmentation, targeting, management, retainment, and data acquisition of profitable customers. Data mining can also help banks quickly determine potential defaulters and accordingly make decisions for the issuance of credit cards, loans, etc.

Data Mining Applications in Customer Segmentation

Traditional methods for market research have always been used for the segmentation of customers, but data mining can be more granular, and it can increase effectiveness. It aids in the segmentation process in a more precise manner and helps in the tailoring of customer requirements. Data Mining can help identify a customer segment based on vulnerability and enhance customer satisfaction through decisions based on the insights generated.


Uses of Data Mining in Education

Educational data mining (EDM) is the latest emerging field that aims to establish techniques to uncover knowledge based on data originating from educational environments. EDM aims to predict students’ potential learning behavior, explore the impact of educational support, and advance scientific knowledge about learning.

Data mining can be implemented by educational institutions to make sound decisions and predict the achievement levels of students to pay more attention to the content and techniques of teaching. These teaching techniques can be developed by observing the studying and behavioral patterns of the students.

Data Mining in Market Basket Analysis

Market basket analysis is one of the key modeling techniques adopted by retailers to identify the relations between certain groups of items. To put it simply, it looks for combinations of items that are frequently ordered together.

Market basket analysis allows the understanding of the purchase behavior of buyers. This information can come in really handy for retailers who want to know the buyers’ requirements and accordingly arrange their stores’ layout. Through the process of differential analysis, comparison can be seamlessly carried out between different stores and customers from different demographic groups.

Uses of Data Mining in Fraud Detection

Fraud Detection

Detection of fraud through traditional methods is very complex and time-consuming. This is where data mining comes in to generate valid information and meaningful insights. Ideally, a robust fraud detection system will thoroughly protect user information. Through means of a collection of sample records, a model is built to identify and classify this information as either fraudulent or non-fraudulent.

Application of Data Mining in CRM

Customer relationship management or CRM implements customer-focused strategies to acquire customers, improve customer loyalty, and retain them. Data mining plays a significant part in business by ensuring healthy relationships with customers. This is done primarily through data mining technology that collects relevant data and information for analysis. More effective solutions can be generated from the insights received.

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Application of Data Mining in Manufacturing Engineering

Often, the manufacturing process can be quite complex, and in those situations, valuable and reliable information and knowledge can be a huge asset. This is where data mining tools can be of use. They help identify patterns and trends and extract the relationships between product portfolio, product architecture, and customer requirements in system-level designing. Data mining can also aid in the prediction of the product development cost, wear and tear of production assets, span time, dependencies, etc. Manufacturers can anticipate maintenance, which can successfully reduce downtime.

Data Mining Applications in Research Analysis

Data mining is instrumental in data cleaning, data pre-processing, and database integration, which makes it ideal for researchers. Data mining can help identify the correlation between activities or co-occurring sequences that can bring about change in the research. Data mining, in conjunction with data visualization and visual data mining, can offer clarity in data and research.

Data Mining Applications in Criminal Investigation

Crime analysis is concerned with exploring and identifying crime characteristics and studying their relationships with criminals. A very high volume of datasets exists for criminology, due to which it can be very complicated. Evidently, data mining has found extensive applications in this field as an appropriate tool. Conversions of all text-based crime reports into word processing files are possible that can further be utilized in crime-matching processes.

Apart from the 10 listed applications of data mining, there are more sectors that make use of it extensively:

  • Marketing
  • Intrusion detection
  • Lie detection
  • Corporate Surveillance
  • Bioinformatics
  • E-commerce
  • Retail
  • Service providers
  • Insurance
  • Communications and many more

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Data mining explains the past and predicts the future for analysis. With that, comes a multitude of applications and uses in diverse fields and industries. Data mining, although not a stand-alone entity, is the quintessential process that works together with pre-processing techniques, such as data preparation, data exploration, etc., and post-processing techniques, including model validation, model performance monitoring, scoring, etc., to bring the best insights and solutions.

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