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首页> 外文期刊>International Journal of Data Mining & Knowledge Management Process >Discovery of Patterns and evaluation of Clustering Algorithms in SocialNetwork Data (Face book 100 Universities) through Data Mining Techniques and Methods
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Discovery of Patterns and evaluation of Clustering Algorithms in SocialNetwork Data (Face book 100 Universities) through Data Mining Techniques and Methods

机译：通过数据挖掘技术和方法发现社交网络数据（Facebook 100大学）中的模式并评估聚类算法

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摘要

Data mining involves the use of advanced data analysis tools to find out new, suitable patterns and project the relationship among the patterns which were not known prior. In data mining, association rule learning is a trendy and familiar method for ascertaining new relations between variables in large databases. One of the emerging research areas under Data mining is Social Networks. The objective of this paper focuses on the formulation of association rules using which decisions can be made for future Endeavour. This research 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 of Face book. It contains self-defined characteristics of a person including variables like residence, year, and major, second major, gender, school. This paper to begin with the research uses only ten Universities and highlights the formation of association rules between the attributes or variables and explores the association 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 of each.The previous research with this dataset has applied only regression models and this is the first time to apply association rules.
机译：数据挖掘涉及使用高级数据分析工具来找出合适的新模式，并计划先前未知的模式之间的关系。在数据挖掘中，关联规则学习是一种确定大型数据库中变量之间新关系的流行且熟悉的方法。数据挖掘下新兴的研究领域之一是社交网络。本文的目标集中在制定关联规则上，通过该规则可以为将来的努力做出决策。这项研究应用了Apriori算法，这是派生关联规则的经典算法之一。该算法适用于Face book 100大学数据集，该数据集源自Face book的Adam D’Angelo。它包含一个人的自定义特征，包括诸如居住地，年份以及专业，第二专业，性别，学校等变量。本文从仅使用十所大学的研究开始，重点介绍了属性或变量之间的关联规则的形成，并探讨了课程与性别之间的关联规则，并发现了性别对课程学习的影响。本文试图涵盖用于聚类的主要算法，并对每个算法进行简单描述。之前对该数据集的研究仅应用了回归模型，这是首次应用关联规则。

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