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store chain in the United States. Forty percent of all U.S households have one of Kroger’s loyalty cards. The Kroger is trying to drive loyalty for life with their customers. In particular, their customers are rewarded with offers on what they buy instead of trying to be sold something else. In other words, each of them could receive coupons different from each other, not the same coupons. In order to match the best customers with the right coupons, the Kroger analyses customers’ behavior using the data-mining techniques. For instance, one recent mailing was customized to 95% of the intended recipients. Such business strategy for looking at customers to win customers for life makes the Kroger beat their largest competitor, Walmart, for the last 6 years largely. [http://www.kypost.com/dpp/news/region_central_cincinnati/downtown/data-mining-is-big-business-for-kroger-%26-getting-bigger-all-the-time]

Korea Customs Service (South Korea)

The Korea Customs Service (KCS) is a government agency established to secure national revenues by controlling imports and exports for the economic development of South Korea and to protect domestic industry through contraband control. It is responsible for the customs clearance of imported goods as well as tax collection at the customs border. For detecting illegal cargo, they implemented a system using SAS for fraud detection, based on its widespread use and trustworthy reputation in the data-mining field. This system enabled more specific and accurate sorting of illegal cargo. For instance, the number of potentially illegal factors increased from 77 to 163. As a result, the detection rate for important items, as well as the total rate, increased by more than 20% [http://www.sas.com/success/kcs.html].

B.4 DATA MINING IN HEALTH CARE AND BIOMEDICAL RESEARCH

With the amount of information and issues in the health-care industry, not to mention the pharmaceutical industry and biomedical research, opportunities for data-mining applications are extremely widespread, and benefits from the results are enormous. Storing patients’ records in electronic format and the development in medical-information systems cause a large amount of clinical data to be available online. Regularities, trends, and surprising events extracted from these data by data-mining methods are important in assisting clinicians to make informed decisions, thereby improving health services.

Clinicians evaluate a patient’s condition over time. The analysis of large quantities of time-stamped data will provide doctors with important information regarding the progress of the disease. Therefore, systems capable of performing temporal abstraction and reasoning become crucial in this context. Although the use of temporal-reasoning methods requires an intensive knowledge-acquisition effort, data mining has been used in many successful medical applications, including data validation in intensive care, the monitoring of children’s growth, analysis of a diabetic patient’s data, the monitoring of heart-transplant patients, and intelligent anesthesia monitoring.

Data mining has been used extensively in the medical industry. Data visualization and artificial neural networks are especially important areas of data mining applicable in the medical field. For example, NeuroMedicalSystems used neural networks to perform a pap smear diagnostic aid. Vysis Company uses neural networks to perform protein analyses for drug development. The University of Rochester Cancer Center and the Oxford Transplant Center use KnowledgeSeeker, a decision tree-based technology, to help with their research in oncology.

The past decade has seen an explosive growth in biomedical research, ranging from the development of new pharmaceuticals and advances in cancer therapies to the identification and study of the human genome. The logic behind investigating the genetic causes of diseases is that once the molecular bases of diseases are known, precisely targeted medical interventions for diagnostics, prevention, and treatment of the disease themselves can be developed.