Web page rank prediction with PCA and em clustering

In this paper we describe learning algorithms for Web page rank prediction. We consider linear regression models and combinations of regression with probabilistic clustering and Principal Components Analysis (PCA). These models are learned from time-series data sets and can predict the ranking of a set of Web pages in some future time. The first algorithm uses separate linear regression models. This is further extended by applying probabilistic clustering based on the EM algorithm. Clustering allows for the Web pages to be grouped together by fitting a mixture of regression models. A different method combines linear regression with PCA so as dependencies between different web pages can be exploited. All the methods are evaluated using real data sets obtained from Internet Archive, Wikipedia and Yahoo! ranking lists. We also study the temporal robustness of the prediction framework. Overall the system constitutes a set of tools for high accuracy pagerank prediction which can be used for efficient resource management by search engines.