Multiple factor hierarchical clustering algorithm for large scale web page and search engine clickstream data

Gang Kou () and Chunwei Lou

Annals of Operations Research, 2012, vol. 197, issue 1, 123-134

Abstract: The developments in World Wide Web and the advances in digital data collection and storage technologies during the last two decades allow companies and organizations to store and share huge amounts of electronic documents. It is hard and inefficient to manually organize, analyze and present these documents. Search engine helps users to find relevant information by present a list of web pages in response to queries. How to assist users to find the most relevant web pages from vast text collections efficiently is a big challenge. The purpose of this study is to propose a hierarchical clustering method that combines multiple factors to identify clusters of web pages that can satisfy users’ information needs. The clusters are primarily envisioned to be used for search and navigation and potentially for some form of visualization as well. An experiment on Clickstream data from a processional search engine was conducted to examine the results shown that the clustering method is effective and efficient, in terms of both objective and subjective measures. Copyright Springer Science+Business Media, LLC 2012

Keywords: Information retrieval; Web page clustering; Multiple criteria decision making; Multiple factor hierarchical algorithm; Clickstream analysis; K-means algorithm (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s10479-010-0704-3

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