The Enhanced Performance of Hierarchical Fusion Based Data Mining for High Density Data Traffic in Big Data Servers

Hierarchical fusion based data mining is a novel approach to analyzing high density data traffic, where multiple sources of data are combined in a hierarchical manner to develop an effective representation for the data. This is typically done by creating a hierarchical structure in the form of a tree, consisting of multiple methods for feature extraction, clustering and data classification. These hierarchical structures can be used to identify patterns in the data that are not easily discernible otherwise. The hierarchical fusion based data mining has been applied to a variety of domains such as image analysis, text mining, and web mining. It has been shown to be effective in finding patterns in high density data traffic, which otherwise may be difficult to detect due to the sheer volume of data. Additionally, the hierarchical structure allows for better scalability when the data sets grow with more data.