The 3 Most Important Data Mining Techniques

Data mining is a process of analyzing large scads of information to generate new information. From intuition, you may think that it is more of the extraction of new data, but that is not the case. Instead, data mining is about determining patterns of finding new knowledge from the data which is already there.

Depending upon technologies and data mining techniques of database management software and machine learning, specialists have directed their careers to come up with a better understanding of how to process and analyze conclusions from huge amounts of data and information. But what are some of the most important techniques that they use to do this?

Data mining techniques


Identifying patterns

One of the main and most important techniques of data mining is becoming proficient at identifying patterns in the sets of data.  This is normally the recognition of reoccurrences in your data that happens at regular intervals, or an appearance of a variable over time. For instance, you may see that the sale of one specific product goes up just before the holidays, or you may notice that people visit your website more during the summer or warm weather.

Classification of data

Classification is a technique that is more complex in data mining. It is a technique that enables you to determine different attributes and put them together in discernable categories, which can then be used to come to conclusions about the data set. They can also serve as functions for the data. For instance, if you are determining data on the financial background of individual customers and their purchase patterns, you may classify them as low and high risks. These classifications can then serve as a more detailed analysis for these customers or finding them.

Outlier detection and association

Association is a technique that tracks patterns, and it linked to dependently linked variables. With this technique, you will be looking for specific events or attributes which correlate with each other or another event. For instance, you may realize that when a customer purchases a specific item of yours, they also buy a second item, which is related to the first item. This is usually the “People also bought” category.

Outlier detection is a simple technique of understanding that the overarching pattern cannot give you a clear understanding of a data set. You should also be able to determine the anomalies or data which deviate your accurate measures. For instance, if your customers are all males, but during a week in July, female customers increase, you may want to see which variable led to the increase in female customers instead of just including female customers in your base. Determining the reason for this spike may help you better understand how to serve these customers as well.


Therefore, now that you know about the three main techniques of data mining, you can start working on these techniques to improve your skills greatly.