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Types of Data Mining

If you haven't heard the term data mining yet, it would be good to have a little discussion about "data mining" before learning the types of data mining. In this article, we will learn the different types of data mining (or data mining methods). However, if you already know what data mining is, you can directly move on to data mining methods (or types).

What is Data Mining?

In General, data mining is nothing but a process of finding or extracting useful information from huge volumes of data. You may get familiar if we use the term big data. Although using a big range of techniques can help us to use this information to increase revenues, cost-cutting and improve customer relationships, etc. It may be quite possible that you may be thinking that is why data mining is so important. The answer to this question is quite complex. However, it is not the answer that is actually big. You may have seen staggering numbers; the volumes of produced data are getting doubled every two years. However, this growth rate of the data is also increasing, or it will be correct to say that data is getting doubled even in less than two years.

Features of Data mining

These are the following key features that data mining usually allows us:

  • Sift through all the chaotic and repetitive noise in your data.
  • Allows understanding what is relevant and then making good use of that information to assess likely outcomes.
  • Accelerate the pace of making informed decisions.

Why do we need Data Mining?

In today's modern world, we are all surrounded by big data, which is predicted to be grown by 40% by the next decade. You may wonder that the real fact is that we are drowning in the data, but at the same time, we are starving for knowledge (or useful Data). The main reason behind this, all this data creates noise which makes it difficult to mine. In short, we have generated tons of amorphous data but experiencing failing big data initiatives as the useful data is deeply buried inside. Therefore without powerful tools such as Data Mining, we cannot mine such data, and as a result, we will not get any benefits from that data.

Types of Data Mining

Each of the following data mining techniques serves several different business problems and provides a different insight into each of them. However, understanding the type of business problem you need to solve will also help in knowing which technique will be best to use, which will yield the best results. The Data Mining types can be divided into two basic parts that are as follows:

  1. Predictive Data Mining Analysis
  2. Descriptive Data Mining Analysis

1. Predictive Data Mining

As the name signifies, Predictive Data-Mining analysis works on the data that may help to know what may happen later (or in the future) in business. Predictive Data-Mining can also be further divided into four types that are listed below:

  • Classification Analysis
  • Regression Analysis
  • Time Serious Analysis
  • Prediction Analysis

2. Descriptive Data Mining

The main goal of the Descriptive Data Mining tasks is to summarize or turn given data into relevant information. The Descriptive Data-Mining Tasks can also be further divided into four types that are as follows:

  • Clustering Analysis
  • Summarization Analysis
  • Association Rules Analysis
  • Sequence Discovery Analysis

Here, we will discuss each of the data mining's types in detail. Below are several different data mining techniques that can help you find optimal outcomes as the results.


This type of data mining technique is generally used in fetching or retrieving important and relevant information about the data & metadata. It is also even used to categories the different types of data format into different classes. If you focus on this article until it ends, you may definitely find out that Classification and clustering are similar data mining types. As clustering also categorizes or classify the data segments into the different data records known as the classes. However, unlike clustering, the data analyst would have the knowledge of different classes or clusters. Therefore in the classification analysis, you have to apply or implement the algorithms to decide in which way the new data should be categorized or classified. A classic example of classification analysis would be Outlook email. In Outlook, they use certain algorithms to characterize an email is legitimate or spam.

This technique is usually very helpful for retailers who can use it to study the buying habits of their different customers. Retailers can also study the past sales data and then lookout (or search ) for products that customers usually buy together. After which, they can put those products nearby of each other in their retail stores to help customers save their time and as well as to increase their sales.


In statistical terms, regression analysis is a process usually used to identify and analyze the relationship among variables. It means one variable is dependent on another, but it is not vice versa. It is generally used for prediction and forecasting purposes. It can also help you understand the characteristic value of the dependent variable changes if any of the independent variables is varied.

3. Time Serious Analysis

A time series is a sequence of data points that are usually recorded at specific time intervals of points. Usually, they are - most often in regular time intervals (seconds, hours, days, months etc.). Almost every organization generates a high volume of data every day, such as sales figures, revenue, traffic, or operating cost. Time series data mining can help in generating valuable information for long-term business decisions, yet they are underutilized in most organizations.

4. Prediction Analysis

This technique is generally used to predict the relationship that exists between both the independent and dependent variables as well as the independent variables alone. It can also use to predict profit that can be achieved in future depending on the sale. Let us imagine that profit and sale are dependent and independent variables, respectively. Now, on the basis of what the past sales data says, we can make a profit prediction of the future using a regression curve.

5. Clustering Analysis

In Data Mining, this technique is used to create meaningful object clusters that contain the same characteristics. Usually, most people get confused with Classification, but they won't have any issues if they properly understand how both these techniques actually work. Unlike Classification that collects the objects into predefined classes, clustering stores objects in classes that are defined by it. To understand it in more detail, you can consider the following given example:


Suppose you are in a library that is full of books on different topics. Now the real challenge for you is to organize those books so that readers don't face any problem finding out books on any particular topic. So here, we can use clustering to keep books with similarities in one particular shelf and then give those shelves a meaningful name or class. Therefore, whenever a reader looking for books on a particular topic can go straight to that shelf. Hence he won't be required to roam the entire library to find the book he wants to read.


The Summarization analysis is used to store a group (or a set ) of data in a more compact way and an easier-to-understand form. We can easily understand it with the help of an example:


You might have used Summarization to create graphs or calculate averages from a given set (or group) of data. This is one of the most familiar and accessible forms of data mining.


In general, it can be considered a method that can help us identify some interesting relations (dependency modeling) between different variables in large databases. This technique can also help us to unpack some hidden patterns in the data, which can be used to identify the variables within the data. It also helps in detecting the concurrence of different variables that appear very frequently in the dataset. Association rules are generally used for examining and forecasting the behavior of the customer. It is also highly recommended in the retail industry analysis. This technique is also used to determine shopping basket data analysis, catalogue design, product clustering, and store layout. In IT, programmers also uses the association rules to create programs capable of machine learning. Or in short, we can say that this data mining technique helps to find the association between two or more Items. It discovers a hidden pattern in the data set.

8. Sequence Discovery Analysis

The primary goal of sequence discovery analysis is to discover interesting patterns in data on the basis of some subjective or objective measure of how interesting it is. Usually, this task involves discovering frequent sequential patterns with respect to a frequency support measure. Some people may often confuse it with time series as both the Sequence discovery analysis and Time series analysis contains the adjacent observation that are order dependent. However, if the people see both of them in a little more depth, their confusion can be easily avoided as the Time series analysis technique contains numerical data, whereas the Sequence discovery analysis contains discrete values or data.


You now have enough knowledge to decide or choose the best technique to summarize the data into useful information - information that can be used to solve a variety of business problems, increase revenue, customer satisfaction or reduce unwanted costs.

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