13 Data Mining Techniques: A Complete Guide

Updated 6 November 2022

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With the rapid increase in technological innovations, companies are generating and receiving more data than they can process and utilise. Data scientists help companies gain actionable insights from this data. When working as a data science, data mining or analytics professional, learning about techniques for data mining can help you derive meaningful insights from the collected data. In this article, we discuss what data mining is and explore 13 data mining techniques, along with some tools used in data mining.

What Is Data Mining?

Data mining involves deriving meaningful insights from a given dataset. Data scientists do this by cleaning raw data, finding patterns, creating models and testing those models. Using these techniques, data scientists and data analysts extract useful information from a large data set. This information allows a company to make data-driven business decisions. Also, data mining is a powerful technique to find relationships and patterns in a data set as it turns raw data into useful information.

Furthermore, it is an advanced analysis technique that uses machine learning and artificial intelligence to extract valuable information from a data set. This information helps a business learn more about their customer needs and provides solutions that resonate with customers.

13 Data Mining Techniques

Data scientists and analysts widely use different data mining techniques to find patterns that help businesses make informed decisions. Some common techniques include:

1. Classification

Classification is a type of data mining method based on machine learning. It involves analysing the different data attributes to classify data into different classes. By applying algorithms, a data analyst analysis how to classify data. After understanding the characteristic of different data types, data analysts classify and categorise data. Usually, analysts use this classification technique to develop software that can classify items into different classes.

For example, suppose one attribute of a data set is "Financial background of customers seeking a loan." In that case, a data analyst may classify this attribute into different classes, namely, "low", "medium" and "high" credit risks. With other details like the salary and credit score, you can train your software or model to accurately predict whether the customer is likely to default on their loan repayments.

2. Clustering

Clustering is an essential technique as it creates meaningful clusters of data objects that share the same characteristics. It means that in a particular cluster, all objects are similar to one another. The clustering technique helps data analysts discover groups and clusters. The degree of association with objects within the same cluster is high. As a result, this data mining method finds its use in customer profiling. Also, data mining applications like information retrieval, spatial database application, scientific data exploration, web analysis and medical diagnostics widely use clustering techniques to cluster similar data objects.

3. Tracking patterns

Tracking patterns is a fundamental technique of data mining. A data analyst identifies and monitors patterns or trends in data to make data-driven inferences that can affect the company's bottom line. Once a company identifies a particular trend or pattern, it can capitalise and build on the trend. For example, if an analyst uncovers that the sale of a particular product is high among teenagers, they can create similar products for them.

4. Association

The association or relation technique relates to statistics. Using this data mining method, data scientists and analysts can find links between two or more data attributes. The association technique helps find events or data attributes that have a high correlation with another event or attribute. It also uncovers the hidden patterns in your data set. For example, you might notice that when a customer avails of a particular service, they often buy a product related to the service.

5. Outlier analysis

Data scientists try to find anomalies or outliers in a data set using the outlier analysis technique. It is often the first step in several data mining applications. An outlier is a data point that provides information and displays abnormal or different behaviour from other data points. Data analysts and scientists can use the univariate or multivariate method to find potential outliers during the data mining process. Financial applications, credit and debit card frauds and network interruption identification widely use the outlier detection technique.

6. Prediction

Prediction is a valuable and important data mining method that discovers the relationship between dependent and independent attributes and between independent attributes alone. This technique scrutinises past and historical trends or patterns in the right sequence to predict a future event. Using predictive analysis, a data scientist can understand future trends that help in making key business decisions. For example, you might review a customer's credit score and financial history to understand credit risk in the future.

7. Regression

Unlike prediction, regression is a data mining method that discovers the relation between the attributes in a given data set. For example, data analysts can use regression to project the price of a product based on other factors, such as availability, demand and inflation. It helps a data scientist uncover the relationship between two or more data attributes. Data modelling and forecasting are two major applications of this technique.

8. Decision trees

A decision tree is a supervised learning algorithm that allows data mining and data science professionals to analyse the data effectively. Decision trees help these professionals understand how the input affects the output, more like an if-then analysis. The root of a decision tree can be a question or a condition with multiple answers. Each answer leads to another set of questions and conditions. This helps in analysing data and allows data mining professionals to come to logical inferences.

9. Sequential patterns

This data mining methodology discovers a series of events that occur in an order or sequence. It identifies similar patterns, events or trends in transaction data over a specific period. For example, historical sales data can give information about products a customer buys after completing their initial purchase. A customer who buys a digital camera is likely to purchase a printer within 30 days.

Using such information, data scientists can help companies (especially retail companies) in shelf placement and promotion. Other areas where data professionals can use sequential patterns include weather prediction, web access pattern analysis, network intrusion detection and production processes.

10. Neural networks

A neural network or artificial neural network is a mathematical model inspired by biological neural networks. Artificial intelligence and deep learning widely use this concept for data mining. Analysts can use neural for customer research data, sales forecasting, time-series prediction and anomaly detection in a data set.

11. Long-term memory processing

Data processing and analysis are immediate, with results changing whenever new data comes in. Instead of conducting analysis again with new data, analysts prefer the long-term memory processing method. Long-term memory processing is the ability to conduct analysis over an extended time. Using this technique, an analyst identifies patterns and trends in the data, which otherwise would be difficult to detect.

12. Data visualisation

Data visualisation represents data and related information in a visual form that is easy to understand and comprehend. As this technique is dynamic, it allows analysts to stream data in real-time. Often, data analysts use dashboards, charts and graphs to uncover useful business information.

Related: Types of Graphs and Charts

13. Data warehouse

A data warehouse collects data from different sources in a company to provide meaningful insights from the data. These warehouses provide analysts with generalised and consolidated data in a multidimensional view. Along with the multidimensional views, a data warehouse provides online analytical processing tools (OLAP) for effective data analysis.

What Are The Tools Used In Data Mining?

Some common tools used in data mining are:

Integrated data mining tools for statistical analysis like SPSS, R and SAS
Open-source data mining tools like RapidMiner
Data mining tools for big data like Apache Spark
Cloud solutions for data mining like Azure ML
Data mining tools for neural networks like PyTorch
Data mining tools for visualisation like Matplotlib

In the marketing field, these data mining tools can help you understand the customer's preference and gather important data on location, gender and demographics. Using these tools, data analysts can leverage information to optimise marketing and sales efforts. Whereas in the human resources field, analysts can use these tools to uncover trends useful for hiring, compensation planning and retention. Data mining is extremely useful in the hiring process, as it gives insights about resumes and applications which keyword screening software may miss.

Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.