Data mining is one of the most useful techniques that help entrepreneurs, researchers, and individuals to extract valuable information from huge sets of data. Data mining is also called Knowledge Discovery in Database (KDD). The knowledge discovery process includes Data cleaning, Data integration, Data selection, Data transformation, Data mining, Pattern evaluation, and Knowledge presentation.
Data Mining is a process used by organizations to extract specific data from huge databases to solve business problems. It primarily turns raw data into useful information.
Data Mining Techniques
1. Association
It is one of the most used data mining techniques out of all the others. In this technique, a transaction and the relationship between its items are used to identify a pattern. This is the reason this technique is also referred to as a relation technique. It is used to conduct market basket analysis, which is done to find out all those products that customers buy together on a regular basis.
2. Clustering
This technique creates meaningful object clusters that share the same characteristics. People often confuse it with classification, but if they properly understand how both these techniques work, they won’t have any issues. Unlike classification which puts objects into predefined classes, clustering puts objects in classes that are defined by it.
3. Classification
This technique finds its origins in machine learning. It classifies items or variables in a data set into predefined groups or classes. It uses linear programming, statistics, decision trees, and artificial neural network in data mining, amongst other techniques. Classification is used to develop software that can be modelled in a way that it becomes capable of classifying items in a data set into different classes.
4. Prediction
This technique predicts the relationship that exists between independent and dependent variables as well as independent variables alone. It can be used to predict future profit depending on the sale. Let us assume that profit and sales are dependent and independent variables, respectively. Now, based on what the past sales data says, we can make a profit prediction for the future using a regression curve.
5. Sequential patterns
This technique aims to use transaction data, and then identify similar trends, patterns, and events in it over a period of time. The historical sales data can be used to discover items that buyers bought together at different times of the year. Businesses can make sense of this information by recommending customers to buy those products at times when the historical data doesn’t suggest they would.
Data Mining Applications
Education: For analyzing the education sector, data mining uses Educational Data Mining (EDM) method. This method generates patterns that can be used both by learners and educators. By using data mining EDM we can perform some educational task:
[if !supportLists]· [endif]Predicting students admission in higher education
Predicting student performance
Teachers teaching performance
Curriculum development
Teachers teaching performance
Curriculum development
Market Basket Analysis
Market basket analysis is one of the key modeling techniques adopted by retailers to identify the relations between certain groups of items Retail industry collects large amounts of data on sales and customer shopping history. Retail data mining helps in analyzing client behavior, customer buying patterns and trends and lead to better customer service, good customer satisfaction and minimizing the cost of business.
Banks
Data mining helps banks work with credit ratings and anti-fraud systems, analyzing customer financial data, purchasing transactions, and card transactions. Data mining also helps banks better understand their customers’ online habits and preferences, which helps when designing a new marketing campaign.
Credit card fraud detection.
Identify ‘Loyal’ customers.
Extraction of information related to customers.
Determine credit card spending by customer groups.
Healthcare
Data mining helps doctors create more accurate diagnoses by bringing together every patient’s medical history, physical examination results, medications, and treatment patterns. Mining also helps fight fraud and waste and bring about a more cost-effective health resource management strategy.
Telecommunication Industry
Today the telecommunication industry is one of the most emerging industries providing various services such as fax, pager, cellular phone, internet messenger, images, e-mail, web data transmission, etc
This is the reason why data mining is become very important to help and understand the business.
Data mining in the telecommunication industry helps in identifying the telecommunication patterns, catch fraudulent activities, make better use of resource, and improve quality of service. Here is the list of examples for which data mining improves telecommunication services −
Fraudulent pattern analysis.
Identification of unusual patterns.
Mobile Telecommunication services.
Use of visualization tools in telecommunication data analysis.