Data Mining: Introduction, Advantages, Disadvantages, and Applications

In this tutorial, we will learn about the introduction, advantages, disadvantages, and applications of data mining. By Harshita Jain Last updated : April 17, 2023

Introduction to Data Mining

In today's world, the amount of data is increasing exponentially whether it is biomedical data, security data or online shopping data, many industries preserve the data in order to analyse it, so that they can serve their customers more effectively through the information which they take out from large preserve data. This taking out or digging out information from huge data sets obtained from different sources and industries is known as Data Mining.

What is Knowledge Discovery?

Knowledge discovery is the overall process of extracting knowledge from the huge data sets. It involves the following steps:

Data Cleaning – In Data Cleaning the noise and inconsistent data is removed.
Data Integration − multiple data sources are combined.
Data Selection − only the relevant data is selected from the database.
Data Transformation − data is consolidated into appropriate forms for mining by performing summary or aggregation operations.
Data Mining − this is an intelligent step in which various methods are applied to extract data patterns.
Pattern Evaluation − data patterns, which can be in different forms like trees, associations, clusters, etc. are evaluated.
Knowledge Presentation − this step finally provides knowledge.

Advantages of Data Mining

The following are the advantages of data mining:

Easy to analysis huge data at one go
Profitable decision-making process
Prediction of trends
Knowledge-based information
Profitable production
Discovery of hidden patterns
Cost effective, time efficient, and effective prediction

Disadvantages of Data Mining

The following are the disadvantages of data mining:

The concise information obtained by the companies, they can sell it to other companies for money like American Express has sold information about their customers credit card purchases to other company.
Data mining requires advance training and prior knowledge about the tools and softwares to work on.
Various data mining tools work in different manners due to different algorithms employed in their design. Therefore, the selection of the correct data mining tools is a very tough task.
Some times prediction can go wrong and can play havoc for the companies on taking any decision based on that prediction.

Applications of Data Mining

1. Communications

Data mining techniques are used in the communication sector to predict customer behavior to offer highly targeted and relevant campaigns.

2. Insurance

Data mining helps insurance companies to price their products profitable and promote new offers to their new or existing customers.

3. Education

Data mining benefits educators to access student data, predict achievement levels and find students or groups of students who need extra attention. For example, students who are weak in a science subject.

4. Manufacturing

By using the help of Data Mining Manufacturers can predict wear and tear of production assets. They can anticipate maintenance which helps them reduce them to minimize downtime.

5. Banking

Data mining helps the finance sector to get a view of market risks and manage regulatory compliance. It helps banks to identify probable defaulters to decide whether to issue credit cards, loans, etc.

6. Retail

Data Mining techniques help retail malls and grocery stores identify and arrange most sellable items in the most attentive positions. It helps store owners to come up with the offer which encourages customers to increase their spending.

7. Service Providers

Service providers like mobile phone and utility industries use Data Mining to predict the reasons when a customer leaves their company. They analyze billing details, customer service interactions, complaints made to the company to assign each customer a probability score and offer incentives.

8. E-Commerce

E-commerce websites use Data Mining to offer cross-sells and up-sells through their websites. One of the most famous names is Amazon, which uses Data mining techniques to get more customers into their eCommerce store.

Data Types in Data Mining