跳到主要內容

臺灣博碩士論文加值系統

(34.204.180.223) 您好!臺灣時間:2021/08/01 14:33
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:徐維坤
研究生(外文):Wei-Kuen Shiu
論文名稱:使用資料探勘技術於網頁可用性評估並應用於適應化網站之研究
論文名稱(外文):Design Usability Evaluation with Data Mining Methods for Adaptive Websites
指導教授:李知炫李知炫引用關係
指導教授(外文):Ji-Hyun Lee
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:設計運算研究所碩士班
學門:設計學門
學類:綜合設計學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:111
中文關鍵詞:網頁探勘網頁可用性設計循環式設計瀏覽效益適應化網站
外文關鍵詞:Web-MiningAdaptive WebsiteRecursive DesignWeb Usability DesignBrowsing Efficiency
相關次數:
  • 被引用被引用:3
  • 點閱點閱:226
  • 評分評分:
  • 下載下載:53
  • 收藏至我的研究室書目清單書目收藏:0
本論文主旨在於建立一套適應化的網站系統,適應化的網站系統意指網站系統本身有能力改變原先的設計,並更加符合使用者的需求。系統根據使用者的上站紀錄加以分析找出瀏覽的行為特徵,藉此找出網站設計上的缺失,並加以改進,讓瀏覽行為更加順暢,找出行為特徵並加以改進的方式有許多種,大部分探討適應化網站的研究偏重在網頁之間的關聯性,希望藉由頁面之間的關聯度來找出網頁適合擺放的位置,本研究則著重在介面上操作的效率,如同一般的人機介面,操作效率意指使用者與電腦之間的互動是否良好,使用者是否可透過較少的操作步驟來達到功能的操作。基於此,找出使用者的操作動作,並根據瀏覽動作之目的來加以分析操作的效能,同時進而開發一套演算法來縮短操作步驟,變更頁面的配置,經由建立新的連結或給予設計師建議移除不好的連結,讓使用者能夠更快速地到達目的頁面,增加介面操作的效率。最後將此演算法經由系統自動執行,讓網站可依瀏覽者的活動行為動態變更網站的架構,讓瀏覽者的瀏覽與操作更有效率。
This thesis proposes to build an adaptive web system – that is, a web site that is capable of changing its original design to fit user requirements. For the purpose of improving shortcomings of the website, and also to make it much easier for users to access information, the system analyzes user browsing patterns from their access records. This research concentrates on the operating-efficiency of a website – that is, the efficiency with which a group of users browse a website. By achieving high efficiency, users spend less operating cost to accomplish a desired user goal. Based on user access data, I analyze each user’s operating activities as well as their browsing sequences. With this data, I can calculate a measure of the efficiency of the user’s browsing sequences. The thesis develops an algorithm to accurately calculate this efficiency and to suggest how to increase the efficiency of user operations. This can be achieved in two ways: (i) by adding a new link between two web pages, or (ii) by suggesting to designers to reconsider existing inefficient links so as to allow users to arrive at their target pages more quickly. Using this algorithm, I develop a research prototype implementation to prove the concept of efficiency. The implementation is an adaptive website system to automatically change the website architecture according to user browsing activities and to improve website usability from the viewpoint of efficiency.
Chinese Abstract (中文摘要) i
English Abstract (英文摘要) ii
Thanks (致謝) iii
Table of Content iv
List of Figures v
List of Tables vi
1 Introduction 1
1.1 Research Background 1
1.2 Motivation 2
1.3 Objective and Scope 4
1.4 Overview 5
2 Related Research and Project 6
2.1 Data-Mining and Knowledge Discovery in Database 6
2.2 Web Mining 11
2.3 Web-based Interface Design 15
2.4 Adaptive websites 20
3 Methodology 23
3.1 Obtaining the information for mining activities 25
3.2 Obtaining Efficiency 27
3.3 The gap between expectation and actuality 33
3.4 Using Gap value to determine web architecture 32
4 System Implementation 43
4.1 System Architecture 43
4.2 Server-Side Control Program 43
4.3 Adaptive Website for Client Side 47
5 Conclusion 51
5.1 Summary 51
5.2 Potential Contribution 52
5.3 Future Work 52
References 54
Appendix 58
Chinese Summary (中文概要) 83
Websites:

1. The company of Point Topic (2003). "Broadband Analysis". Available as http://www.point-topic.com/home/press/dslanalysis.asp
2. The company of Pew Internet Project (2004). "Content Creation Online". Available as http://www.pewtrusts.com/pdf/pew_internet_content_022904.pdf
3. The company of eMarketer (2003). “The value of on-line content”. Available as http://www.emarketer.com/
4. The company of Nielsen NetRatings (2001). "Global Internet Index". Available as http://www.nielsen-netratings.com
5. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth (1996). "From Data Mining to Knowledge Discovery in Databases," AI Magazine. Available as http://citeseer.ist.psu.edu/correct/283224
6. J. Nielsen (2002). "Deep Linking is Good Linking". Available as http://www.useit.com/alertbox/20020303.html.
7. J. Nielsen (2000). "End of Web Design". Available as http://www.useit.com/alertbox/20000723.html.
8. The company of Broadvision (1998). “Increasing site usage”. Available as http://www.broadvision.com.
9. Likeminds (1998). Available as http://www.andromedia.com.
10. The company of Netperceptions (1998). Available as http://www.netperceptions.com.
11. The company of Accrue (1999). Available as http://www.accrue.com.
12. The company of Andromedia (1997). Available as http://www.andromedia.com.
13. Hit list commerce (1998). Available as http://www.marketwave.com.
14. The company of Netgenesis (1998). Available as http://www.netgenesis.com.
15. Surfaid analytics (1999). Available as http://surfaid.dfw.ibm.com.
16. Webtrends log analyzer (1998). Available as http://www.webtrends.com.


Conference papers:

1. J. Han and M. Kamber (2001). “Data Mining: Concepts and Techniques”. Morgan Kaufmann, San Francisco.
2. M. Koutri, S. Daskalaki, and N. Avouris (2002). "Adaptive Interaction with Web Site: an Overview of Methods and Techniques". Computer Science and Information Technologies CSIT.
3. Stanford_Persuasive_Technology_Lab (2002). "How Do People Evaluate a Web Site Credibility?".
4. C. M. Calongne (2001). "Designing For Website Usability". JCSC.
5. D. Hix and H. R. Hartson (1993). “Developing User Interfaces: Ensuring Usability Through Product & Process”. John Wiley & Sons.
6. M. Perkowitz and O. Etzioni (1997), "Adaptive Web Site: an AI Challenge". IJCAI-97.
7. R. Agrawal and R. Srikant (1994). "Fast algorithms for mining association rules". In Proc. of the 20th VLDB Conference, Santiago, Chile, 487-499.
8. R. Cooley, P.-N. Tan, and J. Srivastava (1999). "Discovery of interesting usage patterns from web data". Technical Report 99-022, University of Minnesota.
9. M. Spiliopoulou and L. C. Faulstich (1998). "Wum: A web utilization miner". In EDBT Workshop WebDB98, Valencia, Spain.
10. O. R. Zaiane, M. Xin, and J. Han (1998). "Discovering web access patterns and trends by applying olap and data mining technology on web logs". In Advances in Digital Libraries, Santa Barbara, CA, 19-29.
11. C. Shahabi, A. M. Zarkesh, J. Adibi, and V. Shah (1997). "Knowledge discovery from users web-page navigation". In Workshop on Research Issues in Data Engineering, Birmingham, England.
12. M.S. Chen, J.S. Park, and P. S. Yu (1996). "Data mining for path traversal patterns in a web environment". In 16th International Conference on Distributed Computing Systems, 385-392.
13. B. Huberman, P. Pirolli, J. Pitkow, and R. Kukose (1998). “Strong regularities in world wide web surfing”. Technical report, Xerox PARC.
14. A. Zarkesh, J. Adibi, C. Shahabi, R. Sadri, and V. Shah (1997). "Analysis and design of server informative www-sites". In Sixth International Conference on Information and Knowledge Management, Las Vegas, Nevada.
15. B. Mobasher, R. Cooley, and J. Srivastava (1999). "Creating adaptive web sites through usage-based clustering of urls". In Knowledge and Data Engineering Workshop.
16. T. Joachims, D. Freitag, and T. Mitchell (1997). "Webwatcher: A tour guide for the world wide web". In the 15th International Conference on Artificial Intelligence, Nagoya, Japan.
17. D. Ngu and X. Wu (1997). "Sitehelper: A localized agent that helps incremental exploration of the world wide web". In 6th International World Wide Web Conference, Santa Clara, CA.
18. H. Lieberman (1995). "Letizia: An agent that assists web browsing". In Proc. of the 1995 International Joint Conference on Artificial Intelligence, Montreal, Canada.
19. T. Yan, M. Jacobsen, H. Garcia-Molina, and U. Dayal (1996). "From user access patterns to dynamic hypertext linking". In Fifth International World Wide Web Conference, Paris, France.
20. O. Nasraoui, R. Krishnapuram, and A. Joshi (1999). "Mining web access logs using a fuzzy relational clustering algorithm based on a robust estimator". In Eighth International World Wide Web Conference, Toronto, Canada.
21. T. Fawcett and F. Provost (1999). "Activity monitoring: Noticing interesting changes in behavior". In Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, 53-62.
22. V. Almeida, A. Bestavros, M. Crovella, and A. Oliveira (1996). “Characterizing reference locality in the www”. Technical Report 96-11, Boston University.
23. S. Schechter, M. Krishnan, and M. D. Smith (1998). "Using path profiles to predict http requests". In 7th International World Wide Web Conference, Brisbane, Australia.
24. C. C. Aggarwal and P. Yu (1998). "On disk caching of web objects in proxy servers". In CIKM 97, Las Vegas, Nevada, 238-245.
25. M. Perkowitz and O. Etzioni (1998). "Adaptive web sites: Automatically synthesizing web pages". In Fifteenth National Conference on Artificial Intelligence, Madison, WI.
26. M. Perkowitz and O. Etzioni (1999). "Adaptive web sites: Conceptual cluster mining". In Sixteenth International Joint Conference on Artificial Intelligence, Stockholm, Sweden.
27. A. Buchner and M. D. Mulvenna (1998). "Discovering internet marketing intelligence through online analytical web usage mining". SIGMOD Record 7(4):54-61.
28. B. Padmanabhan and A. Tuzhilin (1998). "A belief driven method for discovering unexpected patterns". In Fourth International Conference on Knowledge Discovery and Data Mining, New York, 94-100.
29. L. Catledge and J. Pitkow (1995). "Characterizing browsing behaviors on the world wide web". Computer Networks and ISDN Systems 27(6).
30. E. H. Chi, J. Pitkow, J. Mackinlay, P. Pirolli, G. Weiler, and S. K. Card (1998). "Visualizing the evolution of web ecologies". In CHI ''98, Los Angeles, California.
31. S. L. Manley (1997). “An Analysis of Issues Facing World Wide Web Servers”. Undergraduate, Harvard.


Journal Papers:

1. K.-L. Wu, P. –S. Yu, and A. Ballman (1998). "Speed-tracer: A web usage mining and analysis tool". IBM Systems Journal 37(1).
2. M. F. Arlitt and C. L. Williamson (1997). "Internet web servers: Workload characterization and performance implications". IEEE/ACM Transactions on Networking.
3. J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan (2000). "Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data". ACM SIGKDD.
4. R. Srikant and Y. Yang (2001). "Mining Web Logs to Improve website Organization". ACM 2001.
5. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth (1994). "From data mining to knowledge discovery: An overview". In Proc. Of the ACM KDD.
6. R. Agrawal (1999). "Data mining: crossing the Chasm". In the 5th ACM SIGKDD Int''l Conference on Knowledge Discovery and Data Mining (KDD99).
7. E. Cohen, B. Krishnamurthy, and J. Rexford (1998). "Improving end-to-end performance of the web using servervolumes and proxy filters". In Proc. of ACM SIGCOMM, 241-253.
8. E. Cohen, B. Krishnamurthy, and J. Rexford (1998). "Improving end-to-end performance of the web using server volumes and proxy filters". In Proc. of ACM SIGCOMM, 241-253.


Books:

1. J. Raskin (2000). “The Human Interface”. 1st ed: Stratford Publishing, Inc.
2. Han, J. and Kamper, M., “Data Mining: Concepts and Techniques”, San Francisco: Morgan Kaufmann, 2001.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top