Data Mining Techniques: Methods, Process & Applications

Today the value of data and information is as high as ever before. While a well-known aphorism claims that time is money, we can say the same thing about data today. Moreover, the volumes of information that our surroundings continuously grow, and it is becoming more and more challenging to find the necessary insights. As a result, people have started using data science and AI-powered tools to facilitate interaction with data (especially business data) and automate numerous time-consuming processes. This necessity to efficiently work with data is one of the most powerful factors for boosting the popularity of data solutions software and machine learning development services. And that’s when data mining techniques enter the game.

In this article, we are going to explain what this approach to working with data presupposes, when it can be of great help and what data mining methods are considered to be the most reliable and useful today.

Data mining: What is it?

What association do you have with the word mining? You will recollect the underground activities related to finding natural resources. These activities have little in common with data mining, and the only thing that unites them is their final goal - getting something you are looking for.

What tasks are we talking about? They predict trends, identify potential risks, find new development methods, and create a client profile and other important features.

Theoretically, you can process all your business data manually, but with data mining tools and techniques, you will be able to:

• significantly reduce the time and human resources needed for data processing;
• avoid the risk of a human error;
• increase the accuracy of the results;
• improve the efficiency of data processing.

When we say that the data mining process helps to make the work with data more efficient, it means that with these tools will not just find valuable insights that you are looking for but also will be able to detect the patterns, identify correlations, or anomalies in the data under consideration.

Data mining includes different steps and components to extract and discover patterns in datasets. It relies on various methods. That’s why it’s important not to confuse statistics and data mining. Statistics forms just one part of the overall process.

Use cases of data mining techniques

Data mining methods can be applied across various data sources and business segments. For example, you can use such tools for better understanding and more efficient processing of the information generated:

• on websites;
• in public databases;
• on eCommerce platforms;
• on social media websites and social networking apps;
• in enterprise systems (CRM, HRMS, ERM, and others);
• in various research papers and reports;
• on IoT devices (including wearable and medical devices).

Now let’s look at how data mining can help specialists in different spheres.

Marketing

Data mining tools help detect and analyze the correlation between gender, age, education, and people’s preferences. As a result, companies can predict client behavior, create mailing lists and personalize loyalty campaigns for them.

Banking

In banking and finance, data mining is used to analyze risks. For example, banks apply such tools for calculating credit ratings and predicting potentially dangerous, insecure transactions.

Education

With solutions powered by data mining, professors and teachers can access student data, information related to their progress, and level of knowledge in different subjects. As a result, educators can predict the time needed for studying different materials and detect those students who require additional attention.

eCommerce

Data mining techniques can offer upsells and cross-sells and get more buyers into particular eCommerce stores. These days, different big data tools are often used in this industry. If you want to learn how to use data to achieve your business goals, we recommend reading this article.

Retail

With data mining, traditional shops can identify what offers seem to be the most interesting to clients and how to place products on different shelves to make them look more attractive. As a result, it is possible to increase sales significantly.

Healthcare

Data mining ensures a higher level of accuracy in diagnostics. With innovative software, medical centers and hospitals get access to all medical records, analysis results, and schemes used for treatment and can detect the most efficient treatments in each case. Also, data mining makes it possible to predict certain illnesses among different population groups and patients’ symptom deterioration and react to these situations timely.

Insurance

Insurance companies that use data mining can better promote their services, attract new clients and increase profits.

Manufacturing

With data mining, manufacturers can better predict equipment failure, optimize operational costs and predict defects.

Logistics and transportation

In this sphere, with data mining, it is possible to optimize routes in real-time, forecast fuel consumption, predict failures related to vehicle technical state, and plan deliveries.

Radio and TV

Television and radio companies use data mining to measure their audiences. For example, they can analyze the preferences of different social groups, predict their interests, and detect the most suitable periods for other advertising.

Data mining process: How it is organized

There can be different approaches to defining steps in the data mining process. We offer to look at the most popular one that presupposes breaking the entire process into six steps.

• Business understanding.
  This step includes fully understanding all the parameters related to a company or project, including the ongoing market situation, key business goals, and main criteria that will determine success.
• Data understanding.
  Identifying the data required to solve the set tasks and getting these from available sources is crucial. 
• Data preparation.
  The data should be put in the necessary format that will be appropriate for solving the tasks. It is crucial to enhance data quality and avoid duplication or losses. 
• Modeling.
  Algorithms are applied to detect patterns within the analyzed data at this stage.
• Evaluation.
  It is important to evaluate whether the received results are appropriate for solving the set tasks. If the results are not good enough, another model will be applied.
• Deployment.
  That final stage presupposes making the results known to those who should make decisions.

The most popular data mining techniques

There are numerous data mining techniques, but we’d like to draw your attention to the most popular ones widely applied today across many industries. They all can be united into two groups based on their aims: there are description and prediction techniques. Some of them are mentioned below.

1. Classification. It includes placing data into several categories based on the set attributes. Identify the key characteristics that will be used to categorize your business data. It will help you manage the assets or persons better than this or that info from the set category is related.
2. Regression. This method is applied to detecting the type and nature of the relationship between data in one dataset. In other words, it helps to demonstrate how variables are related, which is quite useful for making forecasts.
3. Clustering. This method is similar to classification. You need to group data based on characteristics that are similar or common to them. Graphics and colors are used to show the distribution of data.
4. Pattern tracking. This basic technique is intended for recognizing patterns in data sets. Usually, we can talk about repeating cycles in data changing within regular periods in this situation.
5. Prediction. This technique is used for projecting data/results/events that you will be able to observe in the future. It is often enough to recognize and detect historical trends for predicting.
6. Association rule learning (Dependency modeling). It is a machine learning method based on rules. It's aimed at finding relations between different variables in databases. It also helps to detect interesting relationship rules.
7. Anomaly detection (Change and deviation detection). This method aims to detect any unusual cases that differ from the earlier norm identified within the data.
8. Summarization is used for data visualization, exploratory data analysis, and automated reporting. Summarization is used for data visualization, exploratory data analysis, and automated reporting.

To conclude

If you need any assistance from data mining experts, do not hesitate to contact us. At Geomotiv, we have strong expertise in working with data, implementing reliable solutions powered by the most popular data mining methods, and finding the best ways of getting the insights that will bring the highest value for you. So let’s discuss how we can help your business reach new peaks. Fill in this form, and we will contact you as soon as possible.