Background: Provide a health care service to the patients with diabetes provides useful information that could be used to identify, treatment, following up and prevention of diabetes. Explore and investigation of large volumes of DATA requires effective and efficient methods for finding hiding patterns in the DATA. The use of various techniques of DATA MINING in particular Classification and Frequent patterns can be helpful.Methods: This article is a narrative review. We searched keywords related to application of DATA MINING in the field of diabetes, through related DATAbases, in English language articles published from 2005 to 2015. Also related articles in the selected articles list have been analyzed.Results: From the 2144 articles obtained in the initial search, 38 articles related to the subject of study, were selected. Several studies shown that classification and clustering algorithms, association rules and artificial intelligence are the most widely used DATA MINING techniques for predict the risk of diabetes has been successfully used.Conclusion: The important step in control of diabetes, use of the methods that could determine the possibility or lack of diabetes. According to studies conducted in this area seem to use DATA MINING techniques to prevent, treat and discover the connection between diabetes and its risk factors, can lead to significant improvements in the field of diabetes research and provide better health care for this group of patients.

Fraud is normal penman in business main object of this paper, is investigation DATA MINING & financial fraud based on financial ratios.The results paper indicate that, DATA MINING technique for financial fraud is suitable. Also, this technique as core of manager think for business management at fraud.

This study has proposed a new procedure, based on expanded RFM model, deterMINING weight of parameters with pair-wise comparison matrix, clustering the products with K-optimum according to Davies- Bouldin Index, and then classifying customer product loyalty under B2B concept. It is necessary for firms to understand the customers and predict their needs for more success in business. The developed methodology has been implemented in SAPCO Co. The result shows a tremendous capability to the firm to assess the customer loyalty in marketing strategy designed by this company in comparing with random selection commonly used by most companies in Iran.

The objective of this research is marine DATA users clustering using DATA MINING technique. To achieve this objective, marine organizations will enable to know their DATA and users requirements. In this research, CRISP-DM standard model was used to implement the DATA MINING technique. The required DATA was extracted from 500 marine DATA users profile DATAbase of Iranian National Institute for Oceanography and Atmospheric Sciences (INIOAS) from 1386 to 1393. The TwoStep algorithm was used for clustering. In this research, patterns was discovered between marine DATA users such as student, organization and scientist and their DATA request (DATA source, DATA type, DATA set, Parameter and Geographic area) using clustering for the first time. The most important clusters are: Student with International DATA source, Chemistry DATA type, “World Ocean DATAbase” DATAset, Persian Gulf geographic area and Organization with Nitrate parameter. Senior managers of the marine organizations will enable to make correct decisions concerning their existing DATA. They will direct to planning for better DATA collection in the future. Also DATA users will guide with respect to their requests. Finally, the valuable suggestions were offered to improve the performance of marine organizations.