Title: Data Mining
Author: Kriko
Published: May 25, 2023
Last modified: Jul 7, 2026

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# Data Mining

**Data mining** is an analytical approach used to discover meaningful patterns, 
relationships and trends within large datasets. It helps transform raw information
into insights that can support practical decisions. Statistical methods, machine
learning algorithms and database technologies may be used together during this process.
Organisations that generate substantial volumes of data can use data mining to identify
information relevant to their business objectives.

The data mining process generally includes collecting, cleaning, preparing and modelling
data before evaluating the results. Its purpose is to reveal previously unknown 
relationships and apply the findings to a defined business problem. However, not
every pattern discovered is meaningful or reliable. Results must therefore be validated,
interpreted within the correct context and tested through appropriate performance
measures.

Telecommunications companies may analyse customer behaviour to identify users who
are likely to leave a service. Financial institutions and insurers can use data 
mining for risk assessment, fraud detection and pricing activities. Manufacturers
may examine machine data to anticipate equipment failures and maintenance requirements.
Marketing teams can analyse customer characteristics, purchase histories and engagement
information to create segments and identify sales trends.

Data mining may use methods such as **classification, clustering, association and
prediction**. Classification assigns observations to predefined categories, while
clustering groups similar observations without requiring existing labels. Association
analysis examines products or behaviours that frequently occur together. Predictive
methods use historical information to estimate the likelihood of future outcomes.

Process mining examines event logs generated by business systems to understand how
operational processes actually function. An e-commerce company may analyse order,
payment, preparation and delivery records to identify delays or supplier performance
issues. **Text mining** extracts useful information from sources such as comments,
emails, social media posts and customer feedback. User comments on a video platform,
for example, may be grouped according to topic, sentiment or complaint type.

Predictive analytics uses historical data and statistical models to estimate future
results. A company may examine previous product return records to forecast return
rates for the next period. Although this field overlaps with data mining, it may
also include broader analytical methods. When supported by reliable data, suitable
models and professional interpretation, data mining can improve customer satisfaction,
reduce risk and strengthen operational decision-making.

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