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Data mining involves the use of advanced data analysis tools to find out new, suitable patterns and projectthe relationship among the patterns which were not known prior. In data mining, association rule learningis a trendy and familiar method for ascertaining new relations between variables in large databases. Oneof the emerging research areas under Data mining is Social Networks. The objective of this paper focuseson the formulation of association rules using which decisions can be made for future Endeavour. Thisresearch applies Apriori Algorithm which is one of the classical algorithms for deriving association rules.The Algorithm is applied to Face book 100 university dataset which has originated from Adam D’Angelo ofFace book. It contains self-defined characteristics of a person including variables like residence, year, andmajor, second major, gender, school. This paper to begin with the research uses only ten Universities andhighlights the formation of association rules between the attributes or variables and explores theassociation rule between a course and gender, and discovers the influence of gender in studying a course.This paper attempts to cover the main algorithms used for clustering, with a brief and simple description ofeach.The previous research with this dataset has applied only regression models and this is the first time toapply association rules

international journal of data mining and knowledge management process

Discovery of Patterns and evaluation of Clustering Algorithms in SocialNetwork Data (Face book 100 Universities) through Data Mining Techniques and Methods