Computers promise that be as a repository of knowledge and wisdom, but instead, they sent us large amounts of data, web mining is the process of information discovery and knowledge from the Web data. The data is collected from the server, client, proxy server or database in Web mining. Web mining methods are divided into three categories: web content mining, web structure mining and web usage mining. There are several functional areas including e-commerce web mining, text mining, and management of customer behavior. Web mining research focuses on developing knowledge extraction techniques which are used for data analysis. 3 main methods that are used for data mining in web include: association or association rules, sequential patterns, and clustering requirements. The main objective of the web mining is to collect information about the user navigation patterns. Of course, web mining is faced with various challenges and constraints. And many researches are currently doing research in the field of web mining that aim to solve this problem.
Social networks can include anything ranging from family, friends, classes, objects and other similar cases, important and effective members, members of exception, the formation of such networks can be discovered by using the relationships between the members of the network which are important to business and research works, to achieve these cases , social networks should be analyzed using special tools. Social network analysis tools generally includes two packaged based on graphical user interfaces (GUIs) and packages made for programming / scripting. These tools are powerful and extensible and are able to analyze big data networks and visualize networks, isolated or central data and other important data can be simply discovered by data visualization. In this paper, some of the most important tools of social network analysis are presented and compared according to some their capabilities.
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