Data mining is the process of using computational analysis to identify patterns and insights that aid business decisions. It has several different applications depending on the industry. Learning about data mining applications helps identify their scope and may benefit in advancing your career. In this article, Pritish Halder discusses its various applications, find out about the various tools used in data mining, and explore some career opportunities in this field.

What are data-mining applications?

Every industry collects data from various primary and secondary data sources. The expanse of data collected may be structured or unstructured and requires pre-processing before features can be extracted. Data mining is the process of extracting this data and using advanced computational techniques to derive insights. The results help organizations make effective business decisions, reduce risks and improve performance. For efficient data mining applications, the data sources, data quality, and data storage require proper management. Data mining is an effective computational process that has several benefits in varying industries. Some of its applications are:

Health care

Data mining helps improve the quality of health care systems. Predictive analysis helps recommend medicines and evaluate the treatment progress. Similarly, identifying unusual patterns in medical claims, medicine purchases or incoherent prescriptions help track fraudulent practices.


Data mining helps insurance companies understand customer purchase behaviour and predict the insurance policy they are likely to purchase in the future. They track fraudulent claim practices and strengthen their systems to avoid them. Insurance companies can trace policies that get claimed together and bundle them to provide better services.

Market basket analysis

Market basket analysis suggests that if a customer buys a certain quantity of a particular product, they may purchase it again or look for similar products. Understanding this data helps retailers identify the frequency of purchase and manage their stock accordingly. It also helps improve sales and manage customer relations.

Financial analysis

Banks have detailed information about their customers, their transactions and loans. Understanding this bulk of data allows banks to classify customers and customise services like loans, credit card spending limits, rewards and provide discounts on purchases. Identifying unusual activity in a transaction helps track fraudulent activities and security breaches.

Intrusion detection

Data mining techniques help classify information for intrusion section systems. The system then generates an alarm on detecting any foreign elements that do not fit the classification rule. This process helps detect security breaches, attacks, misuse and anomalies. Data mining techniques are crucial for any business and help protect essential information.


Data mining helps track energy consumption patterns and devise systems to increase efficiency. It aids in predicting power consumption in different geographical locations. Elaborate data mining systems provide details on operations patterns too. This insight later helps to optimise operations and invest in equipment that improves production efficiency.

Retail and E-commerce

The retail and e-commerce sector collects and tracks customer details, transactions and product sales. It helps them identify customer purchase behaviour, product preferences and seasonal product sales. This data benefits organisations to forecast sales and customise their offerings. Efficiently using past data to make business decisions helps retailers and e-commerce owners reduce risk and increase profitability.

Spatial data mining

Data mining facilitates the study of past data, discovery and analysis of spatial and geographical information. It helps identify hotspots and unusual locations, taking the spatial relations between objects into account. Additionally, their latitude, area, perimeter and coordinates, help discover previously unknown but potentially useful information.

Biological data analysis

Using complex computational analysis data mining facilitates the study and interpretation of biological datasets. It helps predict protein structure, gene classification and analyses cell mutation. It advances biological studies and improves the healthcare system.

Criminal investigation

The main objective of using data mining in criminal investigation is to fasten the rate of solving a crime. Clustering data helps group crime characteristics and devise ways to prevent them. Data from multiple sources is analysed to simplify complex relations between crime and criminal. This helps identify patterns in crime over a time-period or geographical location.

Supply chain management

Companies with numerous warehouses requiring the transport of goods and materials use data mining to optimise their process. It helps them analyse demand patterns and plan supply accordingly. Companies also use this data to make their distribution channels more efficient and improve coordination with other warehouses and distributors.


Media channels like radio, television and over-the-top (OTT) platforms keep track of their audience to understand consumption patterns. Using this information, media providers make content recommendations, change programme schedules and produce content of the preferred genre. Data mining helps media providers improve the viewer experience.

Advertising and marketing

With the advent of digital marketing and data mining technologies, marketers refine their strategies for better engagement and track the live results of their campaigns. Advertisers also use this data to profile users and show them content or product that might interest them. Data mining is widely used in digital marketing to improve targeting and user experience.


Data mining is used in education to learn student productivity and development. It helps understand how a student is performing, predict their future scores, identify relevant placement opportunities and track teacher performance. Data mining may help derive associations between the teaching methodologies and student performance and identify areas of improvement.


The timely use of analysis derived from data mining can help businesses make informed decisions. Data mining helps understand customer purchase patterns, their behaviours and understand the products or services they are likely to buy. Segmenting customers into different datasets helps customise sales and marketing strategies that reduce risk and increase profitability.

Data mining techniques

Data mining techniques are predictive or descriptive. Predictive techniques use data from an available set and predict future values of another data set of interest. Descriptive methods find similarities in data and model patterns and groups. Here are six commonly used data mining techniques:


This technique helps identify values that frequently occur together. It identifies the similarities or relations between the values and showcases future patterns. It helps in market basket analysis, marketing, and retail.


This technique identifies the class of an object by understanding its attributes. Class labels do not exist in this technique. Clustering trains the system to identify patterns and provide distinct categories for classification.


This technique helps classify data values whose class labels are unknown. The system first trains with data that have class labels. It then identifies similarities in data and helps segregate them into distinguished groups.


Prediction studies individual data values and establishes relationships between them. It uses this to predict future results. This technique is helpful in the healthcare and business sector to make informed decisions.

Sequential patterns

It uses historical data, trends, and patterns to predict future behavior. It identifies trends basis the sequence of the events that occur. Sequential patterns may be valuable in recognizing the purchase behavior of customers.

Data mining tools

There are several open-source and proprietary data mining tools in the market. It helps implement the organization’s data mining strategy. The tools vary in the amount of computational analysis they perform. Therefore, choosing a data mining tool depends on the objective the company wishes to achieve. At its core, these tools help classify, identify trends and run queries on large data sets to provide insights. Some of the popular tools used in the industry are:

  • Orange data mining

  • SAS data mining

  • DataMelt data mining

  • Rattle

  • Rapid miner

  • Oracle Data Mining

  • IBM SPDD Modeler

  • Knime

  • Python

  • Kaggle

  • Weka

  • Teradata