Abstract
Data mining is the extraction of projecting information from large data sets, is a great innovative technology which helps corporations focus on the most important information in their data stockrooms. Data mining makes use of various statistical, machine learning and graphical methods and separate the knowledge in to a form which is very much useful for many real world applications. Social network analysis has become a very popular field of research as it is useful for many applications. In this paper we have overviewed various data mining techniques used for social network analysis.
Highlights
Data mining is a powerful tool that can help to find patterns and relationships within our data
To extract the information represented in graphs we need to define metrics that describe the global structure of graphs, find the community structure of the network, and define metrics that describe the patterns of local interaction in the graphs, develop efficient algorithms for mining data on networks, and understand the model of generation of graphs
Social networks research emerged from psychology, sociology, statistics and graph theory
Summary
Data mining is a powerful tool that can help to find patterns and relationships within our data. Data mining discovers hidden information from large databases[5]. Social network analysis has drawn much attention in graph data management research field. To ensure meaningful data mining results, we must understand our data. There are several factors which has made the study of social networks gain enormous importance by researchers. Few such factors include the availability of huge amount of social network data, the representation of social network data as graphs, and so on[2]
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