期刊名称：Oriental Journal of Computer Science and Technology
印刷版ISSN：0974-6471
出版年度：2017
卷号：10
期号：2
页码：474-479
语种：English
出版社：Oriental Scientific Publishing Company
摘要：Clustering is well-known unsupervised learning method. In clustering a set of essentials is separated into uniform groups.K-means is one of the most popular partition based clustering algorithms in the area of research. But in the original K-means the quality of the resulting clusters mostly depends on the selection of initial centroids, so number of iterations is increase and take more time because of that it is computationally expensive. There are so many methods have been proposed for improving accuracy, performance and efficiency of the k-means clustering algorithm. This paper proposed enhanced K-Means Clustering approach in addition to Collaborative filtering approach to recommend quality content to its users. This research would help those users who have to scroll through pages of results to find important content.
