LAUSR

Today’s organizations, industries and research centers are geographically distributed in different sites. To achieve true knowledge of business, mining massive amounts of data is necessary. In earth-related sciences such as meteorology, the date obtained from the various types of sensors is huge because of the high-frequency rate of data acquisition process and also the geographical distribution of weather stations. Therefore, the data mining and knowledge discovery process of this big and distributed data is a challenging work. In this paper, we propose a new distributed data mining approach called multi-agent hierarchical data mining to classify meteorology data, which has been collected from different sites widely distributed around the country (Iran). Our method utilizes a modified version of REPTree algorithm, which has been optimized to work in multi-agent system. To evaluate the proposed approach, it is implemented on 20 million of meteorology data record. Experimental results show that multi-agent hierarchical data mining approach can achieve significant performance improvement over centralized and parallel methods for knowledge discovery in large amounts of data.