Data mining has emerged as an important tool for identifying useful information from data in almost all industries. Industries are using data mining to increase revenue and reduce costs. According to Seidman (2001), “Data mining is particularly valuable for organizations that collect large quantities of historical information. Banks, insurance companies, credit card companies...use this technology to derive critical information from large, unwieldy data samples.” (p. 6).
“Web (data) mining refers to the whole of data mining and related techniques that are used to automatically discover and extract information from web documents and services. When used in a business context and applied to some type of personal data, it helps companies to build detailed customer profiles, and gain marketing intelligence.”(Wel 2004). One of the best known applications of data mining is in the financial sector; the use of individual credit risk assessments, done by banks, when determining if an applicant is a good “risk”. When applicants fill out loan applications, they are often asked to provide their social security number, address, and other identifying information. In addition, they are also required to give other pieces of information that say something about them. They are asked questions about how long they have been at an employer; whether the applicant is a home owner or a renter, how long have they lived at a current address, their marital status, educational level, etc. When banks require all this information, they can analyze the data that they get and discover correlations between applicants’ personal characteristics and the probability of a loan default. This way of using data mining to evaluate and examine all the variables that affect this outcome enables financial institutions such as banks to process hundreds of thousands of loan applications at one time, lowering operation costs through the use of fewer employees.
Another way data mining is used is in healthcare. According to Ramagerri (2013), “data mining can help healthcare insurers to detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services.” (p. 2). A hospital is a very complicated organization, where medical staff gives very efficient and specialized service for patients. Due to rapid advances in medical technology, organizations like hospitals are not robust enough to adapt to rapid changes. According to Karagupta, “Such rapid changes lead to malpractice of medical staff, sometimes a largescale accident may occur by chain reaction of smallscale accidents.” (p. 443). Data mining is used in the hospital as risk mining.
Risk mining is where data including risk information is analyzed, using data mining methods and the results are used for risk prevention. Risk mining consists of three major processes: risk detection, risk clarification, and risk utilization. The first process is risk detection by using acquired knowledge, patterns or other types of information can be identified that is unexpected.
The second process is risk clarification which means if domain experts need more information with finer granularity they collect more data with detailed information and apply data mining to newly collected data. The last process is risk utilization this is where experts evaluate clarified risk information in a realworld environment to prevent more risk events. If they do not have enough risk information for the prevention, then more analysis is required to finish the process.
In the airline industry, data mining is used to help the Customer Relationship
Management (CRM) processes. Most airlines utilize frequent flyer programs. A frequent flyer program proffers an abundance of data to the airline, allowing a better understanding of customer