Digital Consultancy & Business (EN) Non classé

What are the Different Types of Data Mining Techniques?

By Benjamin Massa
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Importance of Data Mining

Data mining is crucial for companies when they are searching for anomalies, patterns, or any aspects of the larger data. It makes predicting the future easier. Implementing the data mining process into your working schedule will help you to reduce risks, and improve customer relationships along with your general performance.

The main goal here is to find important elements in your data, find their connections, and predict future trends based on them. With data mining, you can come up from the informational chaos with valuable insights.

Decisions that will be based on this process will give you more capabilities to improve your business and overcome your competitors. There are several different types of data mining that could work for you.

Classification Analysis

This type of data mining can help you to search for important and relevant information about data and metadata. It will help you to classify your data as well, so your information will be distributed in different segments, called classes.

For example, Outlook email can sort out emails as legitimate or spam based on classification analysis. It’s useful for retailers as well when they are trying to study the buying habits of different customers.

Association Rule Learning

It can help you when you are working with larger databases. This way you can identify possible connections between each element and see hidden patterns in the data. Based on the valuable information that you can gain from this process, you can make more successful decisions for your future.

Association Rule Learning is frequently used to examine the behavior of customers, determine shopping data analysis, product clustering, etc. In the IT industry association rule learning is used to create programs capable of machine learning.

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Anomaly or Outlier Detection

In this case, you can discover anything that doesn’t go well with an expected pattern or behavior. This way you can get critical information about numerous aspects, and use it for fraud detection, fault detection, and any other disturbances. For example, it can be used to detect credit or debit fraud, and lets you see network disruptions as well.

Clustering Analysis

This type of data mining creates meaningful object clusters, where every element has the same characteristics. For example, you can group customers based on sales data, where it will be considered what they are purchasing more frequently. Clustering analysis can be used for web analytics and text mining. There are companies who use it for customer profiling as well.

Regression Analysis

A regression analysis refers to the process of identifying and analyzing the relationship between different elements. It’s used to determine the dependency between different variables because one variable might depend on another but it doesn’t work both ways.  Also, most companies use it for prediction and forecasting.

Sequence Discovery Analysis

The primary goal of sequence discovery analysis is to discover interesting elements and patterns in data. You can evaluate certain criteria hidden in patterns, like the occurrence frequency of anything and duration. It can give you beneficial information for stock exchanges, shopping patterns, etc.

Summarization Analysis

Summarization analysis is another type of data mining, that can help you to simplify your data to quickly identify useful patterns, also you’ll be able to determine more easily what is normal and what seems to be out of place.

For example, summarization is frequently used to create graphs, or calculate averages from a given set of data. You can use tabular summarization to instantly see frequency distribution, cumulative frequency, etc. Or you can use visualizations, like histograms, line graphs, and column graphs to immediately spot trends.

Time Series Analysis

In this case, you can clearly see how different variables change over time. Time series analysis typically needs a larger number of data points to ensure consistency and reliability. Also, you can be sure that any trends or patterns that can be discovered here won’t be outliers. You can use this method to predict future data based on historical data.

Organizations use this method to determine future events and how beneficial they could be for the company’s future. Nowadays, there are numerous technologies that can help you to collect massive amounts of data and analyze it. This way you’ll have a better understanding of numerous aspects that you’ll need to work on in the future.

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By Benjamin

Digital expert


Benjamin Massa

Benjamin is an experienced strategy consultant with 360° skills and a strong mastery of the digital markets across various industries. He advises our clients on strategic and operational matters and elaborates powerful tailor made solutions allowing organizations and entrepreneur to achieve their goals. Building the digital leaders of tomorrow is his day-to-day job.


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