The Kerman region stands out as one of the most significant mining areas globally, owing to its extensive and abundant mineral resources. Bam County, situated in the southeastern part of Kerman, has historically served as a crucial hub connecting the southeast of Iran with Sistan and Afghanistan, attributed to its distinctive geological and geomorphological characteristics. Enjoying considerable commercial and military importance since the Sassanid era, Bam County has garnered attention in archaeological research as a strategically vital region. The exploration of Bam's archaeological sites becomes imperative for historical governments, highlighting the need to investigate and comprehend ancient centers engaged in metal smelting and mining activities. Consequently, an archaeological survey of the central part of Bam County was initiated in 2018-2019 with the specific objective of identifying metal smelting workshops and ancient mines. This article presents the outcomes of a field survey conducted in the central part of Bam County, shedding light on evidence of metal smelting centers, furnaces, and historical mining activities. The primary research inquiries center around the chronology of mining evidence in the central part of Bam County, the types of metals extracted, and the processes involved in metal mining and metallurgy within this region. Employing field and documentary methods, the research adopts a descriptive-analytical approach. The study identified and examined eight sites showcasing evidence of smelting and slag, one ancient mine, and two active mines. These sites have been associated with the extraction and processing of metals and elements such as tin, zinc, lead, silver, iron, and, to a lesser extent, gold. Notably, the substantial volume of zinc and zinc oxide processing in seven sites holds significance. Although cultural materials for chronological dating were absent in the investigated sites, historical sources indicate that the extraction and smelting of these metals in the region date back to at least the 3rd century AH (9th century AD) and persisted until the Qajar period

Deregulation within the banking industry's and unprecedented growth competition in new technologies, every day the importance of keeping current customers and attract new customers are added. This study presents a two-step model to identify characteristics of different groups of bank customers, based on their profitability. The new criteria introduced to analyze the profitability of each customer. And then, this criterion has been used for clustering customers based on their profitability. Because in k-mean algorithm there is not a general rule for the optimal number of clusters and the number of clusters depend on the problem, therefore firstly with applied by Two-step clustering algorithm determine the optimal number of clusters. In this study, Customers clustering to 3 groups as golden, silver, and bronze customers. Then, by using K-mean algorithm different groups of customers are identified. And with help of Apriori algorithm association rules of each cluster is inference. The result of this exploration, help to banks have better understanding of current and future customer expectations. And through this may be facilitated develop of marketing strategies to attract and retain profitable customers.

One of the main problems in dynamic customer segmentation is finding the dominant patterns of customer transmission between different segments over time. Accordingly, we concentrate on the customer dynamics in this paper and try to find different groups of customers in transmission between segments overtime. The dominant characteristics of these groups are also investigated. To achieve this objective, a new hybrid technique based on the K-means algorithm, hierarchical clustering and association rule mining is presented and implemented on the data obtained from one of the main telecommunication corporations in Iran. The results show that there are seven different groups of customers. Furthermore, the impact of customer dynamics on segments' changes is investigated over time. In this regard, a new approach of categorizing customers is proposed according to their impact on the structure and the content of segments' changes. These new groups include "the customers who preserve the structure", "the ones who are consistent with the structure" and "the customers who destroy the structure".

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.