What is data mining?

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In simple words, Data mining is a powerful new technology with great potential to help companies focus on most important information of their business. Data minig is used for research & surveys, information collection, data scanning, competitive analysis, online research and updating data.

Definition of data mining

According to Wikipedia,

“Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.”

Data mining is also known as Knowledge Discovery in Data (KDD). Data mining is utlised in different fields like science and research. Because of data mining, businesses can learn more about their customers and develop more effective strategies related to various business function such as increase sales and decrease costs. Data mining depends on effective data collection,warehousing as well as computer processing.

Data mining tools allow companies to predict future trends. Data mining software can be used for market analysis, science exploration, fraud detection, customer retention and production control. Data mining is also used in the fields of credit card services and telecommunication to detect frauds. 

Parameters of data mining

1 Association rules – Association rules are created by analyzing data for patterns, then using the support and confidence criteria to locate the most important relationships within the data. It is also known as market basket analysis.

2 Data mining clustering – Clustering is the task of discovering groups and structures in the data that are in some way or other similar without using known structures in the data.

3 Classification – Classification parameter looks for new patterns, and might result in a change in the way the data is organized.  It basically generalizes known structure to apply to new data.

4 Predictive analysis – Fostering parameters within data mining can discover patterns in data that can lead to reasonable predictions about the future.

The data mining process



1 Problem definition

Focus on following points at the very first stage:

- Understand project objectives and requirements

- Specify the project from a business perspective

For example:

Business problem = “How can I sell more of my products to customers?

Data mining problem = “Which customers are most likely to purchase the product?”

You can build a model on customers who have purchased the product in past. While building a model, you can include attributes like - age, marital status, number of children, residence information etc.

2 Data gathering & preparation

- This stage involves data collection and exploration.

- At this stage, you should consider removing additional data which is not useful for your business problem.

- Data preparation includes many tasks such as a table, case, attribute selection, data cleansing, and transformation.

- The data preparation consumes 80% to 90% time of the project.

- In data preparation, data tasks are performed multiple times in no prescribed order.

- Data gathering task is carried out to notice the patterns based on business understanding.

3 Model building & evaluation

- At this stage, you are supposed to select modeling techniques to use for prepared data.

- In model building, it makes sense to reduce set of data since the final case table might contain thousands or millions of cases.

- After selecting right model, the test scenario must be generated to validate the quality & validity of the model.

- According to your requirement, you can create more than one model.

- Next step would be an evaluation of the model. Now evaluate how well the model satisfies the originally stated business goal. Gaining business understanding is a crucial process in data mining.

- At this stage, new business requirements may be raised due to the new patterns that have been discovered in the model results.

- At the end of the evaluation phase, data mining experts decide how to use data mining results. Utilising data in effective manner is very important part of data mining process.

4 Deployment

- Through data mining process, you will gain the knowledge of information. It needs to be presented in such a way that your team can use it whenever they want.

- At this stage, insight and actionable information can be derived from the data.

-This stage involves scoring, the extraction of model details or the integration of data mining models within the applications, data warehouse infrastructure and reporting tools.

- Data mining experts use the mining results into a database table or into other applications.

- The CRISP–DM (Cross Industry Standard Process - Data Mining) model offers a structured approach to plan a data mining project.

Uses of Data Mining

Data mining is used in many industries such as retail, finances, communication and marketing. There are many ways to use data mining to determine the impact of sales, customer satisfaction and corporate profits. Some of the common ways to use data mining are given as under:

Sales forecasting

Database Marketing

Product Production

Basket Analysis

Merchandise Planning


Market Segmentation

Customer Loyalty

Card Marketing

Call Detail Record Analysis

CRM – Customer Relationship Management

Fraud Detection

Financial Banking

Bio Informatics

Corporate Surveillance

Customer Profiling

Cross Market Analysis

Benefits of data mining

- Data mining helps to uncover hidden patterns and relationships in data that can be used to make predictions for business.

- Data mining helps marketing companies to build models based on historical data to predict who will respond to the new marketing campaigns.

- Data mining also benefits to retail companies. Retail stores use data mining to understand customers’ buying habits so that they can optimize the layout of their stores to improve customer experience and increase profit as well.

- The manufacturing industries use data mining tools to improve product safety, identify quality issues and manage the supply chain.

- Tax governing bodies use data mining techniques to detect fraud transactions and to find out suspicious tax returns.

- In finance and banking industries, data mining is used to create risk models for loans & mortgages.

- Data mining also helps to predict future trends.

Tools - Data Mining Software

We have listed most popular data mining software for you:


2 Orange

3 Weka


5 Sisense

6 Apache Mahout

7 Rattle

With data mining software solutions and customer experience management solutions - data mining provides a competitive advantage. In future, data mining will be useful to gain more profit. Data mining and analysis can be very useful for any business firm.To analyze large data manually would be an impractical choice. At that time data mining provides valuable insights into large data sets. Tutorialspoint provides amazing tutorials for the beginners to gain mastery in data mining. If you are beginner in data mining then learning from those tutorials will be a good start.

Author Details


Gaurav Patel

Gaurav Patel is Founder of thirstyDevs InfoTech
thirstyDevs InfoTech is a Web solutions company offering innovative web solutions. Our company focuses on providing the best web solution for our customers.

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