Data Mining Techniques

6 minute read
Data Mining Techniques

We all produce various kinds of data with a single click on computers, phones or other gadgets. If this accumulated data is organized, processed and observed, we can obtain certain patterns and trends. These trends, in turn, can help service providers and business horses to line their products according to people’s demand. Professionals belonging to the field of Data Science have expertise in taking care of this part. The syllabus of Data Science courses comprises of a set of methodologies that help in the extraction of data. known as Data mining techniques. Let’s understand them better through this blog!

What is Data Mining? 

Data mining is the process of extraction of data (non-trivial, implicit, previously unknown, and potentially useful) from a large database. It is also known by other names such as Knowledge Extraction, Data-pattern Analysis, Information Harvesting and Knowledge Discovery (KD), etc. It’s a process of employing one or more computer learning techniques to automatically interpret and extract knowledge contained within a database. GIven below are some of its highlights:  

  • It is a multi-disciplinary skill that incorporates the fields of Artificial Intelligence, Statistics, Machine Learning, and Database Technology
  • It is concerned with the discovery of  the hidden knowledge and usually works on large volumes of data
  • It is used in making crucial organizational decisions particularly those of strategic nature
  • Data mining is finding the unknown, valid, and potentially useful patterns in huge data sets. It establishes correlations amongst the data.
Data Mining

Data Mining Techniques are an integral part of a career in Big Data Analytics

List of Data Mining Techniques

Data mining incorporates the use of refined data testing tools to discover previously unknown patterns and their connection among large data sets. These tools can combine Statistical Models, Artificial intelligence AI strategies, and Scientific Calculations. Along these lines, it provides professionals to examine and forecast data. Major techniques used in modern projects include classification, association, clustering, prediction, regression. and sequential patterns.


The classification mining technique is used to gather important and appropriate information about data and metadata. This helps to organize the data in different classes. For example, spatial data, multimedia, world wide web, text data and time-series data. It uses neural networks, genetic algorithms, machine learning, statistics, data warehouse-oriented or database-oriented, and data visualization. Classification of Data mining  can be done as per the following methods: 

  • Type of data sources mined
  • Database involved
  • Kind of knowledge discovered
  • Kind of techniques used

Association Rules

Association rules are if-then statements that determine the possibility of interactions between data items within large data sets in various types of databases. This data mining technique helps to identify a link between two or more items. It finds an unknown pattern in the data set. The algorithm processes various data sets, For example, a list of grocery or daily expenditures to measures a percentage of items being purchased together.  The three main measurement techniques of association rule are given below in the table

Confidence This measurement technique measures the degree of confidence while associating with variables it states how often item B is purchased when item A is purchased as well.

Measuring Formula
(Item A + Item B) / (Item A)

Lift This measurement technique measures the accuracy of the confidence over how often item B is purchased.

Measuring Formula
(Confidence) / (item B) / (Entire dataset)



This measurement technique measures how often multiple items are purchased and compared it to the overall dataset.

Measuring Formula
(Item A + Item B) / (Entire dataset)


Regression is a form of data mining technique that deals with the study of the data mining process employed to identify and analyze the relationship between variables. It is used to define the possibility of the special variable that is present in the set due to an external factor. Some of the important principle factors about regression mining are:

  • Regression is an analysis of numerical data consisting of values of the dependent variable or explanatory variables hence considered a  form of planning and modelling. 
  • Predicted target is of continuous value type that is obtained  from both dependent and independent variable

Outer Detection

Outer detection data mining technique refers to the observation of data items in the data set, which does not match an expected or familiar data pattern and does not exhibit expected behaviour. Outlier detection is a data point that differs from the regression dataset present in the secured sample of data along with real-world datasets. The outlier detection technique plays an important role  as it has a wide range of applications in real-world problems which include some application related to data and security some of such applications are mentioned below:

  • Detecting network interruption identification of credit or debit card fraud. 
  • Detecting outlying in wireless sensor network data,  intrusion, detection and  fraud detection

Sequential Patterns

The sequential pattern is the most prominent data mining technique meant for evaluating sequential data with an aim to discover internal and external sequential patterns. It also involves  Frequent patterns which are patterns (e.g., itemsets, subsequences, or substructures) that appear frequently in a data set. It involves finding subsets that are unknown to the present dataset. In other words, this technique of data mining helps to recognize or identify similar patterns in transaction data over some time.


Another prominent data mining technique used to predict the future by analyzing the already available data and used to obtain a new set of data based on the previous data structure. Prediction is a data mining technique that is the combination of latent data mining techniques such as classification, trends, and clustering, It further analyzes past events or instances in the right sequence to predict a future event.

Course  Universities 
Master of Science in Data Science Michigan State University, USA
MS in Business Analytics  North Eastern University
Graduate Certificate in Big Data Analytics  Georgian College 
Master of Science in Data Science  University of Massachusetts
Certificate in Big Data Solution Architecture   Conestoga College 
Graduate Certificate in Data Analytics   St. Clair College
Post-Degree Diploma in Data Analytics   Langara College

Here is a list of MBA in Data Science Courses!

Careers in Data Mining 

A career in data mining techniques offers the safest road to secure your future with lucrative opportunities. Here are some of the established job profiles: 

  • Big Data Engineer                                   
  • Data Architect                                        
  • Data Warehouse Manager                     
  • Annual Salary Database Manager         
  • Business Intelligence Analyst                
  • Data Scientist                                        
  • Data Modeler                                         
  • Database Developer                              
  • Database Administrator                         
  • Data Analyst          

Hope this blog helped you to acquire all the knowledge about data mining techniques. If you want to make a career in Data Science let leverageEdu be your guide. Get in touch with us and we will tell you the best opportunities that can help you reach your greatest potentials!

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