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

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  • Gang Kou
  • Chunwei Lou

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

Suggested Citation

  • Gang Kou & Chunwei Lou, 2012. "Multiple factor hierarchical clustering algorithm for large scale web page and search engine clickstream data," Annals of Operations Research, Springer, vol. 197(1), pages 123-134, August.
    • Handle: RePEc:spr:annopr:v:197:y:2012:i:1:p:123-134:10.1007/s10479-010-0704-3
    DOI: 10.1007/s10479-010-0704-3
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    1. Sang-Sung Park & Kwang-Kyu Seo & Dong-Sik Jang, 2007. "Fuzzy Art-Based Image Clustering Method For Content-Based Image Retrieval," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 213-233.
    2. Yong Shi, 2009. "Current Research Trend: Information Technology And Decision Making In 2008," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 1-5.
    3. Wen Zhang & Taketoshi Yoshida & Xijin Tang, 2009. "Distribution Of Multi-Words In Chinese And English Documents," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 249-265.
    4. Jongwon Lee & Heeseok Lee, 2008. "Strategic Agent Based Web System Development Methodology," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 309-337.
    5. Yong Shi & Yi Peng & Gang Kou & Zhengxin Chen, 2005. "Classifying Credit Card Accounts For Business Intelligence And Decision Making: A Multiple-Criteria Quadratic Programming Approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 4(04), pages 581-599.
    6. Yi Peng & Gang Kou & Yong Shi & Zhengxin Chen, 2008. "A Descriptive Framework For The Field Of Data Mining And Knowledge Discovery," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 639-682.
    7. Gang Kou & Yi Peng & Yong Shi & Morgan Wise & Weixuan Xu, 2005. "Discovering Credit Cardholders’ Behavior by Multiple Criteria Linear Programming," Annals of Operations Research, Springer, vol. 135(1), pages 261-274, March.
    8. Sergio Alejandro Gómez & Carlos Iván Chesñevar & Guillermo Ricardo Simari, 2008. "Defeasible Reasoning In Web-Based Forms Through Argumentation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(01), pages 71-101.
    9. Qingyu Zhang & Richard S. Segall, 2008. "Web Mining: A Survey Of Current Research, Techniques, And Software," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 683-720.
    10. Soongoo Hong & Pairin Katerattanakul & Seok Jeong Joo, 2008. "Evaluating Government Website Accessibility: A Comparative Study," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 491-515.
    11. Raid Al-Aomar & Fikri Dweiri, 2008. "A Customer-Oriented Decision Agent For Product Selection In Web-Based Services," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(01), pages 35-52.
    12. Jia Hu & Ning Zhong, 2008. "Web Farming With Clickstream," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 291-308.
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    2. Yi Peng, 2015. "Regional earthquake vulnerability assessment using a combination of MCDM methods," Annals of Operations Research, Springer, vol. 234(1), pages 95-110, November.
    3. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2019. "Technology in the 21st century: New challenges and opportunities," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 321-335.
    4. R. Sujatha & T. M. Rajalaxmi, 2016. "Hierarchical Fuzzy Hidden Markov Chain for Web Applications," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 83-118, January.
    5. John A. Aloysius & Hartmut Hoehle & Soheil Goodarzi & Viswanath Venkatesh, 2018. "Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes," Annals of Operations Research, Springer, vol. 270(1), pages 25-51, November.
    6. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    7. Borja Ena & Alberto Gomez & Borja Ponte & Paolo Priore & Diego Diaz, 2022. "Homogeneous grouping of non-prime steel products for online auctions: a case study," Annals of Operations Research, Springer, vol. 315(1), pages 591-621, August.
    8. Roman Vavrek, 2019. "Evaluation of the Impact of Selected Weighting Methods on the Results of the TOPSIS Technique," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(06), pages 1821-1843, November.
    9. Ziyun Deng & Tingqin He, 2018. "A Method for Filtering Pages by Similarity Degree based on Dynamic Programming," Future Internet, MDPI, vol. 10(12), pages 1-12, December.

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