期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2016
卷号：5
期号：6
页码：10228
DOI：10.15680/IJIRSET.2015.0506138
出版社：S&S Publications
摘要：In the rapid growth of social network, discovering emerging topics and classification of data is mostimportant challenging factor. In social network stream, we can detect anomalies and emerging topics based on linksbetween the users that are generated dynamically. For the emerging topic detection purpose to propose a new method inthe area of streaming data. To find anomalies in social streams by using various clustering methods. This paper alsoimplements the data mining technique like text clustering methods to clustering the dataset which contains medicalrecords of patients. Here, we applied Hierarchical text clustering methods like C- Mean, Hierarchical Agglomerativeclustering, and single linkage algorithms are used for clustering and classification of the dataset. LKC algorithm andcompatibility is introduced to provide privacy preservation. The performance analysis carried out by time taken for theclustering and classification of various clustering algorithms based on attributes present in the dataset.
