文章基本信息

标题：An Optimizing Technique for Weighted Page Rank with K-Means Clustering
本地全文：下载
作者：Supreet Kaur ; Usvir Kaur
期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：7
出版社：S.S. Mishra
摘要：Clustering is an essential task in Data Mining process which is used for the purpose to make groups or clusters of the given data set based on the similarity between them. K-Means clustering is a clustering method in which the given data set is divided into K number of clusters. This paper is intended to give the introduction about K-means clustering and its algorithm. The experimental results of K-means clustering and its performance in case of execution time are discussed here. But there are certain limitations in K-means clustering algorithm such as it takes more time for execution. So in order to reduce the execution time we are using the weighted page rank with k means clustering And also shown that how clustering is performed in less execution time as compared to the traditional method. This work makes an attempt at studying the feasibility of K-means clustering algorithm in data mining using the weighted page rank with k means clustering. K means with page rank algorithm gave results with better result set of various numbers of data-sets. In our case we are going to work on k means clustering of database with weighted page content rank algorithm
关键词：Clustering; K-means Clustering; Ranking method; Weighted page ranking; Execution Time