Data mining is the extraction of one or many interesting patterns from large amounts of data. Data mining is now used by many consumer-focused companies (retail), financial, communications and marketing organizations, to “excavate” transaction data and determine prices, consumer preferences and product placement, influence on sales, customer satisfaction, and process data for things related to increasing the usability for the company even to increase the company’s profits.
With data mining, sellers or companies can use transaction data to build products and promotions to specific target consumers/segments. Here are 14 benefits of applying data mining:
Data mining has great potential to improve healthcare systems. Use data and analysis to identify best practices that improve care and reduce costs. Researchers use data mining approaches such as multi-dimensional databases, machine learning, soft computing, data visualization and statistics. Mining can be used to predict the volume of patients in each category.
Processes are developed that ensure that patients receive the right care at the right place and at the right time. Data mining can also help health insurance companies to detect fraud and abuse.
- Market Analysis
Market analysis is a modeling technique based on the theory that if a person buys a certain group of items, then he tends to buy another group of items. This technique allows retailers to understand the buying behavior of buyers. This information can help retailers know the needs of shoppers and modify store layouts accordingly.
By using differential analysis a comparison of results between different stores, between customers in different demographic groups can be carried out.
There is a new emerging field, called Educational Data Mining, which deals with development methods that discover knowledge from data originating in the educational environment. The goals of EDM were identified as predicting students’ future learning behavior, studying the impact of educational support, and advancing scientific knowledge about learning.
Data mining can be used by an institution to take accurate decisions and also to predict student outcomes. As a result, institutions can focus on what to teach and how to teach it. Students’ learning patterns can be taken and used to develop their teaching techniques.
- Manufacturing Engineering
Knowledge is the best asset a manufacturing company has. Data mining tools can be very useful for finding patterns in complex manufacturing processes. Data mining can be used in system-level design to extract relationships between product architecture, product portfolios, and customer requirements data. It can also be used to predict product development span times, costs, and dependencies between other tasks.
Customer Relationship Management is about acquiring and retaining customers, also increasing customer loyalty and implementing customer focused strategies. In order to maintain the right relationship with customers a business needs to collect data and analyze information.
This is where data mining comes into play. With data mining technology, the collected data can be used for analysis. Rather than being confused about where to focus on retaining customers, solution seekers get filtered results.
- Fraud Detection
Billions of dollars have been lost to fraud. Traditional methods of fraud detection are time consuming and complex. Data mining helps in providing meaningful patterns and transforming data into information. Any valid and useful information is knowledge. A perfect fraud detection system must protect the information of all users.
The supervised method includes the collection of sample records. This record is classified as fraudulent or not fake. A model is built using this data and an algorithm is created to identify whether the recording is wrong or not.
- Intrusion Detection
Any action that would compromise the integrity and confidentiality of a resource is a nuisance. Defensive measures to avoid tampering include user authentication, avoid programming errors, and information protection.
Data mining can help improve intrusion detection by adding a level of focus to anomaly detection. This helps analysts distinguish activity from normal, day-to-day network activity. Data mining also helps extract data that is more relevant to the problem.
- Lie Detection
Catching a criminal is easy whereas bringing out the truth from him is difficult. Law enforcement can use mining techniques to investigate crimes, monitor the communications of suspected terrorists. This includes text mining as well.
This process seeks to find meaningful patterns in the data, which is usually unstructured text. Sample data collected from previous studies were compared and a model for the detection of clots was created. With this process model can be created as needed.
- Customer Segmentation
Traditional market research can help us to segment customers but data mining goes deep and increases market effectiveness. Data mining tools in aligning customers into different segments and can adjust the needs according to customers.
The market always retains the consumer. Data mining makes it possible to find customer segments based on vulnerabilities and businesses can offer them with special offers and increase satisfaction.
With the ubiquity of computerized banking, huge amounts of data are supposed to be generated with new transactions. Data mining can contribute to solving business problems in banking and finance by discovering patterns, causation, and correlations in business information and market prices that are not immediately apparent to managers because the data volume is too large or generated too quickly for experts to filter.
Managers can find this information for better segmentation, targeting, acquiring, retaining, and maintaining customers.
- Company Supervision
Corporate supervision is the monitoring of the behavior of a person or group by the company. The data collected is most often used for marketing purposes or sold to other companies, but is also regularly shared with government agencies. It can be used by businesses to customize their products as desired by their customers.
The data may be used for direct marketing purposes, such as targeted advertising on Google and Yahoo, where ads are targeted to search engine users by analyzing their search and email history.
- Research Analysis
History shows that we have witnessed revolutionary changes in research. Data mining is very helpful in data cleaning, data pre-processing and database integration. Researchers can find similar data from databases that might bring about changes in the study.
Identification of co-occurring sequences and the correlation between any activity can be determined. Data visualization and visual data mining give us a clear picture of the data.
- Criminal Investigation
Criminology is a process that aims to identify the characteristics of crime. Actually crime analysis includes exploring and detecting crime and its relationship to criminals. The high volume of crime datasets as well as the complexity of the relationships between such data make criminology an appropriate field to apply data mining techniques to. Text-based crime reports can be converted into word processing files. This information can be used to carry out a crime matching process.
The Data Mining approach seems ideal for Bioinformatics, as it is rich in data. Biological data mining helps to extract useful knowledge from large datasets collected in biology, and other related life science fields such as medicine and neuroscience.
Data mining applications for bioinformatics include gene discovery, protein function inference, disease diagnosis, disease prognosis, optimization of disease treatment, reconstruction of protein and gene interaction networks, data cleaning, and prediction of protein sub-cellular locations.