Data Mining Process Steps

what is data mining and its importance

28-03-2020 data mining techniques

Data mining definition is that the method being used in many companies with the aim of converting raw data into useful information. Data mining is actually based on effective data collection method, warehousing and computer processing method. Data mining involves in the process of exploring and analyzing large blocks of data to collect meaningful patterns and trends. Data mining is highly used in a multiple ways which includes marketing database, credit risk management, spam Email filtering, fraud detection and more.

The data mining process consist of 5 major steps. Initially, an organization collects the data and loads it into the appropriate data warehouses. Secondly, storing and managing the data is carried out either on in the form of in-house servers or the cloud. The third process is that the Business analysts, management teams and information technology professionals access the data and analyze how they need to systematize.

The fourth step here is, application software will sort the data on the basis of the result of the user. Finally, the end-user provides the data in an easy-to-share format, likely in a graph or table format.

Data Warehousing and Mining Software

Data mining programs will examine the associations and patterns in data based on the request from the users. For instance, to create the classes of data, a company can utilize data mining software. Other than this, the data miners find clusters of data on the basis of the logical associations or look at the relationships and sequential patterns to illustrate the conclusions about trends in consumer behavior.

data mining applications

Warehousing is an essential feature of data mining process. Warehousing is actually defined as when companies integrate their information into one database. By means of the data warehouse, a company can rotate off segments of the data for specific users to evaluate and use.

Types of data mining


A database is otherwise known as the database management system (DBMS). And each and every DBMS data stores which are associated to each other in a way or the other. It is a set of software programs that are utilized to handle data and offer easy access to it. These software programs offers great services such as characterizing the structure for database, and it ensures that the saved data is secured and reliable, and organizing various types of data access, such as shared, distributed, and concurrent data access.

Data warehouse management

A data warehouse is actually a single data storage which collects the data from various sources and stores them in the type of a unified plan.

Second process

Once the data is stored in a data warehouse, it undergoes clean-up, assimilation, loading, and stimulating processes. Data stored in a data warehouse is prepared in numerous parts. In this each and every record has an exclusive ID.

Transactional data

Transactional database is defined as the method of data storage in which the data stored that are captured as transactions. These transactions are generally comes under flight booking, customer purchase, click on a website, etc.

Other types of data storage

There are many more types of data storages are available and that are known for the investigations of their structure, semantic meanings, and versatility. They are utilized in a lot of applications. Here, we have characterized some of the data storage types they are, data streams, data designing, sequence data storage, spatial data, multimedia data, and much more.

Data Mining Techniques

data preprocessing in data mining


The association methodology is one of the widely used techniques. It is also one of the mainly used data mining method out of all the other techniques. In this method, a transaction and the relationship between its items are used to recognize a pattern.

Relation methodology

The main reason behind the association process is known as relation methodology. It is used to conduct market basket analysis. It is made to analyze all those products that customers buy together on a regular basis.


This clustering methodology formulates meaningful object clusters that share the same individuality. There will be great confusion behind the method of classification and clustering. Unlike classification method that puts objects into predefined stages, clustering puts objects in classes that are defined by it.


This is a main technique involves in the method of machine learning. It characterizes the items or variables in to a data set and into the types of predefined groups or classes. It utilizes linear programming methodology, statistics techniques, decision trees and some other methodologies among other techniques.


In this method of prediction, it involves to predict the relationship that prevails among independent and dependent variables and independent variables alone. Prediction is utilized to expand software techniques that can be modeled in a way that it can accomplish to predict the items in a data set into different classes.

Data Mining Applications

technoogies used in data mining

Data mining techniques involves in multiple fields. In today’s world, it is highly occupies majority of the parts in our every walk of our life. Data mining is also involves in Fraud detection, manufacturing engineering, and more. Most importantly, it is involved in the applications of health care, education, market basket analysis, manufacturing engineering, finance and banking and more.

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