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

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 do...

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Bibliographic Details
Published in:Annals of operations research Vol. 197; no. 1; pp. 123 - 134
Main Authors: Kou, Gang, Lou, Chunwei
Format: Journal Article
Language:English
Published: Boston Springer US 01.08.2012
Springer Science + Business Media
Springer Nature B.V
Subjects:
Algorithms
Business and Management
Clustering
Clusters
Collection
Combinatorics
Data collection
Data mining
Electronics
Information retrieval
Machine learning
Operations research
Operations Research/Decision Theory
Search engines
Searching
Theory of Computation
Websites
World Wide Web
Multiple criteria decision making
Clickstream analysis
K-means algorithm
Information retrieval
Multiple factor hierarchical algorithm
Web page clustering
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary: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.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-010-0704-3