Mohammad Reza Assadpour - Department of Computer, Qeshm international Branch, Islamic Azad University, qeshm ,Ira
Ali Asghar Safaei - Department of Medical Informatics, Tarbiat Modares University, Tehran-Iran
Mehdi Hossein Zadeh - Department of Computer, Islamic Azad University Science and Research Branch, Tehran-Iran

As you know, medical data is one of the data that should be stored with high speed and accuracy. Since all people have a medical history, the volume of these data is extremely high and management requires the use of appropriate and efficient methods. However, this huge amount of data can really be useful for people and corporations, but also problematic. The problem with this progress is the analysis and analysis of large data. Using data mining techniques, you can extract useful information and hidden relationships between data. The traditional methods of data mining, due to their low speed, cannot directly run on large data, and we must look for a solution that we can analyze with large data. In this paper, the clustering of large medical data has been investigated using a hierarchical clustering algorithm and the results have been compared with some of the methods available in this field. The results show that the proposed method of this paper can cluster with greater accuracy, lower execution time and higher data rates

Macro data, clustering, clustering algorithm, large data volume, data mining