Top 10 Data Mining Techniques

By |2022-06-20T10:06:33+00:00June 3rd, 2022|

Data mining is applied in numerous business areas, including operations, finance, sales and marketing, supply chain, and more. When done correctly, it offers a wealth of information that gives you a competitive edge by helping you develop better strategic decisions.


What is Data Mining and Why Is It Important?

Data mining is a technique used to process data and explore patterns in large datasets to develop practical and data-driven insights. The vital aspects of data mining include data cleaning, data transformation, and data integration.

Data Mining for Business Analytics

Data mining involves extrapolating trends and new information from known data to unravel business intelligence and analytics. It helps businesses solve problems, minimize risks, and explore new possibilities over a period of time.

We’ve jotted down the top 10 data mining techniques that data scientists leverage to extract relevant, actionable data for decision-making.

Top 10 Data Mining Techniques

1)   Pattern Tracking

Pattern tracking is one of the fundamental data mining techniques. It entails recognizing and monitoring trends in datasets to make intelligent analyses regarding business outcomes. For a business, this process could relate to anything from identifying top-performing demographics or understanding seasonal variations in the customer’s buying behavior.

For instance, the pattern in sales data may show that a certain product is more popular amongst specific demographics or a decrease in total sales volume after the holiday season. The company can then use this information to target specific markets and optimize the supply chain.

2)   Association

Like pattern tracking, the association technique involves looking for certain occurrences with connected attributes. The idea is to look for linked variables depending on specific attributes or events. Association rules can be particularly useful for studying consumer behavior.

For example, an online store might learn that customers who purchase a certain product will likely buy a complementary item. Using this insight, they can provide better recommendations to maximize the sales revenue. This technique can also be used in catalog design, product clustering, layout design, etc.


3)   Classification

It’s a useful data mining technique used to derive relevant data and metadata based on a defined attribute, for example, type of data sources, data mining functionalities, and more. Basically, it’s the process of dividing large datasets into target categories. This categorization is also determined by the data framework, for example, relational database, object-oriented database, etc.

Suppose your company wants to forecast the change in revenue for customers given a loyalty membership. You can create a category that contains customers’ demographic data with a loyalty membership to design a binary classification model to predict an increase or decrease in spending.

4)   Outlier Detection

There are instances when the data pattern doesn’t provide a clear understanding of the data. In such situations, the outlier detection technique comes handy. It involves identifying anomalies or “outliers” in your dataset to understand specific causations or derive more accurate predictions.

Here’s an example. Suppose your sales always range between $7,000 to $10,000 per week. But one week, the sales revenue crosses $40,000, without any obvious seasonal factors at play. You’ll want to understand the reason behind the surge in sales so you can replicate it and gain a better understanding of your customer base.

5)   Clustering

Like classification, clustering is a data mining technique that involves grouping data based on similarities. It helps in knowledge discovery, anomaly detection, and gaining insights into the internal structure of the data.

For instance, you might cluster audiences from different regions into packets based on their age group, gender, and disposable income, so you can tailor your marketing campaign to maximize your reach.

The results of cluster data analysis are typically shown using graphs to help users visualize data distribution and identify trends in their datasets.

6)   Sequential Patterns

As the name suggests, this is a mining technique that focuses on discovering patterns or a series of events taking place in a sequence. It’s extensively used in transactional data mining but has numerous applications. For instance, it can help companies recommend relevant items to customers to maximize sales.

An example would be a sequential trend identified at an electronics store where customers who purchase an iPhone are likely to purchase a MacBook within six months. The retailer can use this intelligence to create targeted marketing campaigns to upsell to iPhone buyers. For example, bundle offer of Apple products to maximize turnover.


7)   Decision tree

A decision tree is a data mining technique in machine learning (ML) that focuses on input and output modeling relationships using if/then rules. With this approach, you can learn how the data inputs influence outputs. The trees are typically designed in a top-down, flowchart-like structure.

For example:

  • If the shopping cart value < $500, then it is a regular customer (no discount)
  • If the shopping cart value > $500 AND < $2,000 then it is a premium customer (10 percent discount)
  • If the shopping cart value > $2,000 then it is an exclusive customer (20 percent discount and 10% of the transaction value converted into loyalty points)

This decision tree is a simplified example. A predictive analytics model with several decision tree models facilitates more complex data analytics.

Decision trees are mainly used for classification and regression models.

8)   Regression Analysis

It’s one of the most popular data mining techniques in machine learning that utilizes the linear relationship between variables. It helps you predict the future value of variables. The technique has numerous applications in financial forecasting, resource planning, strategic decision-making, and more.

For example, you can use regression analysis to understand the correlation between education, income, and spending habits. The complexity of the prediction increases as you add more variables. The common techniques include standard multiple, stepwise, and hierarchical regression.

9)   Long-term Memory Processing

Long-term memory processing is a data mining technique in machine learning used to analyze data over long periods. It allows you to identify time-based data patterns, such as climate data, more effectively. It’s intended to scale data in the system memory and utilize additional information in the analysis.

For instance, you can design a predictive model to identify fraudulent transactions by assigning probabilities. You can use this model for existing transactions and then, after some time, update the model with the data derived from new transactions, resulting in improved decision-making.

10) Neural Networks

A neural network is also one of the popular data mining techniques in machine learning models used with Artificial Intelligence (AI). Like neurons in the brain, it seeks to identify relationships in data. Neural networks have different layers working together to produce data analytics results with great accuracy.

These models look for patterns in a large amount of data. While they can be highly complex as a result, the output generated Can provide extremely valuable insights to organizations.


Data Mining Vs. Machine Learning: The Key Difference

Machine learning is the process of training systems to learn and become more accurate at predicting outcomes through experience. In ML, the algorithms and models are designed to adapt to new data independently and through iterations — without human intervention.

Data mining and machine learning fall under the umbrella of data science, but they are considerably different. Data mining is used to determine outcomes based on historical data, while ML uses harvested data and experience to make a system smarter. While the former relies on big data, the latter works with algorithms.

What are the Best Data Mining Techniques in Machine Learning?

It depends. No one technique is suitable for every data mining analytics use case. You must try different data mining applications and go through trial and error to drive relevant, data-driven insights for your business.


Getting Started with Data Mining

Your organization can get started with data mining by leveraging the right data management tools. It’s crucial to have a system that allows a seamless flow of data into analytics systems to facilitate advanced analysis. Modern data warehousing is particularly useful as it serves as a centralized repository for cleaned and transformed data ready for data mining applications.

Also, you can have the best data mining tool, but without accurate and structured datasets, you won’t get the optimal results. That’s because the data must be accurate, complete, and consistent for software to produce insights that are reliable and trustworthy. So, raw data coming from disparate sources must be cleaned and prepared before data mining techniques can be applied to them.

The process may include different aspects of data extraction, data migration, data integration, and more. These steps are imperative to ensure data quality. Without cleaning and preparing data, organizations cannot trust the analytics results and conclusions drawn from the insights.

As most organization data is available in an unstructured format, the first step is to extract data. It involves using a reliable data extraction solution to pull data from PDF business documents and systems and convert it into a machine-readable format.

As a comprehensive data management solutions provider, Astera Software understands the intricacies of extracting and preparing data to derive business value. Try Astera ReportMiner today to see how it can help you seamlessly extract data and optimize your data mining process.