Data mining is an essential process for many businesses, including McDonald’s and Amazon. It involves analyzing huge chunks of unprocessed information to discover valuable insights. It’s no surprise large organizations rely on data mining, considering it helps them optimize customer service, reduce costs, and streamline their supply chain management.

Although it sounds simple, data mining is comprised of numerous procedures that help professionals extract useful information, one of which is classification. The role of this process is critical, as it allows data specialists to organize information for easier analysis.

This article will explore the importance of classification in greater detail. We’ll explain classification in data mining and the most common techniques.

Classification in Data Mining

Answering your question, “What is classification in data mining?” isn’t easy. To help you gain a better understanding of this term, we’ll cover the definition, purpose, and applications of classification in different industries.

Definition of Classification

Classification is the process of grouping related bits of information in a particular data set. Whether you’re dealing with a small or large set, you can utilize classification to organize the information more easily.

Purpose of Classification in Data Mining

Defining the classification of data mining systems is important, but why exactly do professionals use this method? The reason is simple – classification “declutters” a data set. It makes specific information easier to locate.

In this respect, think of classification as tidying up your bedroom. By organizing your clothes, shoes, electronics, and other items, you don’t have to waste time scouring the entire place to find them. They’re neatly organized and retrievable within seconds.

Applications of Classification in Various Industries

Here are some of the most common applications of data classification to help further demystify this process:

  • Healthcare – Doctors can use data classification for numerous reasons. For example, they can group certain indicators of a disease for improved diagnostics. Likewise, classification comes in handy when grouping patients by age, condition, and other key factors.
  • Finance – Data classification is essential for financial institutions. Banks can group information about consumers to find lenders more easily. Furthermore, data classification is crucial for elevating security.
  • E-commerce – A key feature of online shopping platforms is recommending your next buy. They do so with the help of data classification. A system can analyze your previous decisions and group the related information to enhance recommendations.
  • Weather forecast – Several considerations come into play during a weather forecast, including temperatures and humidity. Specialists can use a data mining platform to classify these considerations.

Techniques for Classification in Data Mining

Even though all data classification has a common goal (making information easily retrievable), there are different ways to accomplish it. In other words, you can incorporate an array of classification techniques in data mining.

Decision Trees

The decision tree method might be the most widely used classification technique. It’s a relatively simple yet effective method.

Overview of Decision Trees

Decision trees are like, well, trees, branching out in different directions. In the case of data mining, these trees have two branches: true and false. This method tells you whether a feature is true or false, allowing you to organize virtually any information.

Advantages and Disadvantages

Advantages:

  • Preparing information in decision trees is simple.
  • No normalization or scaling is involved.
  • It’s easy to explain to non-technical staff.

Disadvantages:

  • Even the tiniest of changes can transform the entire structure.
  • Training decision tree-based models can be time-consuming.
  • It can’t predict continuous values.

Support Vector Machines (SVM)

Another popular classification involves the use of support vector machines.

Overview of SVM

SVMs are algorithms that divide a dataset into two groups. It does so while ensuring there’s maximum distance from the margins of both groups. Once the algorithm categorizes information, it provides a clear boundary between the two groups.

Advantages and Disadvantages

Advantages:

  • It requires minimal space.
  • The process consumes little memory.

Disadvantages:

  • It may not work well in large data sets.
  • If the dataset has more features than training data samples, the algorithm might not be very accurate.

Naïve Bayes Classifier

The Naïve Bayes is also a viable option for classifying information.

Overview of Naïve Bayes Classifier

The Naïve Bayes method is a robust classification solution that makes predictions based on historical information. It tells you the likelihood of an event after analyzing how many times a similar (or the same) event has taken place. The most frequent application of this algorithm is distinguishing non-spam emails from billions of spam messages.

Advantages and Disadvantages

Advantages:

  • It’s a fast, time-saving algorithm.
  • Minimal training data is needed.
  • It’s perfect for problems with multiple classes.

Disadvantages:

  • Smoothing techniques are often required to fix noise.
  • Estimates can be inaccurate.

K-Nearest Neighbors (KNN)

Although algorithms used for classification in data mining are complex, some have a simple premise. KNN is one of those algorithms.

Overview of KNN

Like many other algorithms, KNN starts with training data. From there, it determines the distance between particular objects. Items that are close to each other are considered related, which means that this system uses proximity to classify data.

Advantages and Disadvantages

Advantages:

  • The implementation is simple.
  • You can add new information whenever necessary without affecting the original data.

Disadvantages:

  • The system can be computationally intensive, especially with large data sets.
  • Calculating distances in large data sets is also expensive.

Artificial Neural Networks (ANN)

You might be wondering, “Is there a data classification technique that works like our brain?” Artificial neural networks may be the best example of such methods.

Overview of ANN

ANNs are like your brain. Just like the brain has connected neurons, ANNs have artificial neurons known as nodes that are linked to each other. Classification methods relying on this technique use the nodes to determine the category to which an object belongs.

Advantages and Disadvantages

Advantages:

  • It can be perfect for generalization in natural language processing and image recognition since they can recognize patterns.
  • The system works great for large data sets, as they render large chunks of information rapidly.

Disadvantages:

  • It needs lots of training information and is expensive.
  • The system can potentially identify non-existent patterns, which can make it inaccurate.

Comparison of Classification Techniques

It’s difficult to weigh up data classification techniques because there are significant differences. That’s not to say analyzing these models is like comparing apples to oranges. There are ways to determine which techniques outperform others when classifying particular information:

  • ANNs generally work better than SVMs for making predictions.
  • Decision trees are harder to design than some other, more complex solutions, such as ANNs.
  • KNNs are typically more accurate than Naïve Bayes, which is rife with imprecise estimates.

Systems for Classification in Data Mining

Classifying information manually would be time-consuming. Thankfully, there are robust systems to help automate different classification techniques in data mining.

Overview of Data Mining Systems

Data mining systems are platforms that utilize various methods of classification in data mining to categorize data. These tools are highly convenient, as they speed up the classification process and have a multitude of applications across industries.

Popular Data Mining Systems for Classification

Like any other technology, classification of data mining systems becomes easier if you use top-rated tools:

WEKA

How often do you need to add algorithms from your Java environment to classify a data set? If you do it regularly, you should use a tool specifically designed for this task – WEKA. It’s a collection of algorithms that performs a host of data mining projects. You can apply the algorithms to your own code or directly into the platform.

RapidMiner

If speed is a priority, consider integrating RapidMiner into your environment. It produces highly accurate predictions in double-quick time using deep learning and other advanced techniques in its Java-based architecture.

Orange

Open-source platforms are popular, and it’s easy to see why when you consider Orange. It’s an open-source program with powerful classification and visualization tools.

KNIME

KNIME is another open-source tool you can consider. It can help you classify data by revealing hidden patterns in large amounts of information.

Apache Mahout

Apache Mahout allows you to create algorithms of your own. Each algorithm developed is scalable, enabling you to transfer your classification techniques to higher levels.

Factors to Consider When Choosing a Data Mining System

Choosing a data mining system is like buying a car. You need to ensure the product has particular features to make an informed decision:

  • Data classification techniques
  • Visualization tools
  • Scalability
  • Potential issues
  • Data types

The Future of Classification in Data Mining

No data mining discussion would be complete without looking at future applications.

Emerging Trends in Classification Techniques

Here are the most important data classification facts to keep in mind for the foreseeable future:

  • The amount of data should rise to 175 billion terabytes by 2025.
  • Some governments may lift certain restrictions on data sharing.
  • Data automation is expected to be further automated.

Integration of Classification With Other Data Mining Tasks

Classification is already an essential task. Future platforms may combine it with clustering, regression, sequential patterns, and other techniques to optimize the process. More specifically, experts may use classification to better organize data for subsequent data mining efforts.

The Role of Artificial Intelligence and Machine Learning in Classification

Nearly 20% of analysts predict machine learning and artificial intelligence will spearhead the development of classification strategies. Hence, mastering these two technologies may become essential.

Data Knowledge Declassified

Various methods for data classification in data mining, like decision trees and ANNs, are a must-have in today’s tech-driven world. They help healthcare professionals, banks, and other industry experts organize information more easily and make predictions.

To explore this data mining topic in greater detail, consider taking a course at an accredited institution. You’ll learn the ins and outs of data classification as well as expand your career options.

Related posts

Value of the Capstone Project: OPIT Student Interview With Irene
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Jun 12, 2025 6 min read

During the Open Institute of Technology’s (OPIT) 2025 graduation day, the OPIT team interviewed graduating student Irene about her experience with the MSc in Applied Data Science and AI. The interview focused on how Irene juggled working full-time with her study commitments and the value of the final Capstone project, which is part of all OPIT’s master’s programs.

Irene, a senior developer at ReActive, said she chose to study at OPIT to update her skills for the current and future job market.

OPIT’s MSc in Applied Data Science and AI

In her interview, Irene said she appreciated how OPIT’s course did not focus purely on the hard mathematics behind technologies such as AI and cloud computing, but also on how these technologies can be applied to real business challenges.

She said she appreciated how the course gave her the skills to explain to stakeholders with limited technical knowledge how technology can be leveraged to solve business problems, but it also equipped her to engage with technical teams using their language and jargon. These skills help graduates bridge the gap between management and technology to drive innovation and transformation.

Irene chose to continue working full-time while studying and appreciated how her course advisor helped her plan her study workload around her work commitments “down to the minute” so that she never missed a deadline or was overcome by excessive stress.

She said she would recommend the program to people at any stage in their career who want to adapt to the current job market. She also praised the international nature of the program, in terms of both the faculty and the cohort, as working beyond borders promises to be another major business trend in the coming years.

Capstone Project

Irene described the most fulfilling part of the program as the final Capstone project, which allowed her to apply what she had learned to a real-life challenge.

The Capstone Project and Dissertation, also called the MSc Thesis, is a significant project aimed at consolidating skills acquired during the program through a long-term research project.

Students, with the help of an OPIT supervisor, develop and realize a project proposal as part of the final term of their master’s journey, investigating methodological and practical aspects in program domains. Internships with industrial partners to deliver the project are encouraged and facilitated by OPIT’s staff.

The Capstone project allows students to demonstrate their mastery of their field and the skills they’ve learned when talking to employers as part of the hiring process.

Capstone Project: AI Meets Art

Irene’s Capstone project, “Call Me VasarAI: An AI-Powered Framework for Artwork Recognition and Storytelling,” focused on using AI to bridge the gap between art and artificial intelligence over time, enhancing meaning through contextualization. She developed an AI-powered platform that allows users to upload a work of art and discover the style (e.g. Expressionism), the name of the artist, and a description of the artwork within an art historical context.

Irene commented on how her supervisor helped her fine-tune her ideas into a stronger project and offered continuous guidance throughout the process with weekly progress updates. After defending her thesis in January, she noted how the examiners did not just assess her work but guided her on what could be next.

Other Example Capstone Projects

Irene’s success is just one example of a completed OPIT Capstone project. Below are further examples of both successful projects and projects currently underway.

Elina delivered her Capstone project on predictive modeling of natural disasters using data science and machine learning techniques to analyze global trends in natural disasters and their relationships with climate change-related and socio-economic factors.

According to Elina: “This hands-on experience has reinforced my theoretical and practical abilities in data science and AI. I appreciate the versatility of these skills, which are valuable across many domains. This project has been challenging yet rewarding, showcasing the real-world impact of my academic learning and the interdisciplinary nature of data science and AI.”

For his Capstone project, Musa worked on finding the optimal pipeline to fine-tune a language learning model (LLM) based on the specific language and model, considering EU laws on technological topics such as GDPR, DSA, DME, and the AI Act, which are translated into several languages.

Musa stated: “This Capstone project topic aligns perfectly with my initial interests when applying to OPIT. I am deeply committed to developing a pipeline in the field of EU law, an area that has not been extensively explored yet.”

Tamas worked with industry partner Solergy on his Capstone project, working with generative AI to supercharge lead generation, boost SEO performance, and deliver data-driven marketing insights in the realm of renewable energy.

OPIT’s Master’s Courses

All of OPIT’s master’s courses include a final Capstone project to be completed over one 13-week term in the 90 ECTS program and over two terms in the 120 ECTS program.

The MSc in Digital Business and Innovation is designed for professionals who want to drive digital innovation in both established companies and new digital-native contexts. It covers digital business foundations and the applications of new technologies in business contexts. It emphasizes the use of AI to drive innovation and covers digital entrepreneurship, digital product management, and growth hacking.

The MSc in Responsible Artificial Intelligence combines technical expertise with a focus on the ethical implications of modern AI. It focuses on real-world applications in areas like natural language processing and industry automation, with a focus on sustainable AI systems and environmental impact.

The MSc in Enterprise Cybersecurity prepares students to fulfill the market need for versatile cybersecurity solutions, emphasizing hands-on experience and soft-skills development.

The MSc in Applied Data Science and AI focuses on the intersection between management and technology. It covers the underlying fundamentals, methodologies and tools needed to solve real-life business problems that can be approached using data science and AI.

Read the article
OPIT Career Services: How We Support Your Future
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Jun 12, 2025 6 min read

In May 2025, Greta Maiocchi, Head of Marketing and Administration at the Open Institute of Technology (OPIT), went online with Stefania Tabi, OPIT Career Services Counselor, to discuss how OPIT helps students translate their studies into a career.

You can access OPIT Career Services throughout your course of study to help with making the transition from student to professional. Stefania specifically discussed what companies and businesses are looking for and how OPIT Career Services can help you stand out and find a desirable career with your degree.

What Companies Want

OPIT degrees are tailored to a wide range of individuals, with bachelor’s degrees for those looking to establish a career and master’s degrees for experienced professionals hoping to elevate their skills to meet the current market demand.

OPIT’s degrees establish the foundation of the key technological skills that are set to reshape industries shortly, in particular artificial intelligence (AI), big data, cloud computing, and cybersecurity.

Stefania shared how companies recruiting tech talent are looking for three types of skills:

  • Builders – These are the superstars of the industry today, capable of developing the technologies that will transform the industry. These roles include AI engineers, cloud architects, and web developers.
  • Protectors – Cybercrime is expected to cost the world $10.5 trillion by the end of 2025, which means companies place a high value on cybersecurity professionals capable of protecting their investment, data, and intellectual property (IP).
  • Decoders – Industry is producing more data than ever before, with global data storage projected to exceed 200 zettabytes this year. Businesses seek professionals who can extract value from that data, such as data scientists and data strategists.

Growing Demand

Stefania also shared statistics about the growing demand for these roles. According to the World Economic Forum, there will be a 30-35% greater demand for roles such as data analysts and scientists, big data specialists, business intelligence analysts, data engineers, and database and network professionals by 2027.

The U.S. Bureau of Labor Statistics, meanwhile, predicts that by 2032, the demand for information security will increase by 33.8%, by 21.5% for software developers, by 10.4% for computer network architects, and by 9.9% for computer system analysts. Finally, the McKinsey Global Institute predicts a similar 15-25% increase in demand for technology professionals in the business services sector.

How Career Support Makes a Difference

Next, Stefania explained that while learning essential skills is vital to accessing this growing job market, high demand does not guarantee entry. Today, professionals looking for jobs in the technology field must stand out from the hundreds of applicants for each position with high-level skills.

Applicants demonstrate technical expertise in relevant fields by completing OPIT’s courses. They also need to prove that they can deliver results, demonstrating not just what they know but how they have applied what they know to transform or benefit a business. Professionals also need adaptability, adaptive problem-solving skills, and a commitment to continuous learning. OPIT’s final Capstone projects can be an excellent way to demonstrate the value of newly acquired skills.

Each OPIT program prepares students for future careers by providing dedicated support and academic guidance at every step.

What Kind of Support Does Career Services Offer?

Career Services is specifically focused on assisting students in making the transition to the job market, and you can make an appointment with them at any time during your studies. Stefania gave some specific examples of how Career Services can support students on their journey into the career market.

Stefania said she begins by talking with students and discussing what they truly value to help them discover the type of career that aligns with their strengths. With students who are still undecided on how to start to build their careers, she helps them craft a tailored job and internship search plan.

Stefania has also worked with students who want to stand out during the job application process among the hundreds of applicants. This includes hands-on help in reframing resumes, tailoring LinkedIn profiles, and developing cover letters that tell a unique story.

Finally, Stefania has assisted students in preparing for interviews, helping them research the company, develop intelligent questions about the role to ask the interviewer and engage in mock interviews with an experienced recruiter.

Connecting With Employers

OPIT Career Services also offers students exposure to a wide range of employers and the opportunity to build relationships through masterclasses, career talks, and industry roundtables. The office also helps students build career-ready skills through interactive, hands-on workshops and hosts virtual career fairs with top recruiters.

Career Services also plays an integral role in connecting students with companies for their Capstone project in the final phase of their master’s program. So far, students have worked with companies including Sintica, Cosmica, Cisco, PayPal, Morgan Stanley, AWS, Dylog, and Accenture. Projects have included developing predictive modeling for natural disasters and fine-tuning AI to answer questions about EU tech laws in multiple languages.

What Kinds of Jobs Have OPIT Graduates Secured?

Stefania capped off her talk by sharing some of the positions that OPIT graduates have now fulfilled, including:

  • Chief Information Security Officer at MOMO for MTN mobile services in Nigeria
  • Data Analyst at ISX Financial in Cyprus
  • Head of Sustainability Office at Banca Popolare di Sondrio in Italy
  • Data Analyst at Numisma Group in Cyprus
  • Senior Software Engineer at Neaform in Italy

OPIT Courses

OPIT offers both foundational bachelor’s degrees and advanced master’s courses, which are both accessible with any bachelor’s degree (it does not have to be in the field of computer science).

Choose between a BSc in Modern Computer Science for a strong technical base or a BSc in Digital Business to focus on applications.

Meanwhile, courses that involve a final Capstone project include an MSc in Applied Data Science and AI, Digital Business and Innovation, Enterprise Cybersecurity, and Responsible Artificial Intelligence.

Read the article