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題名 考慮網站結構之使用者網站漫遊行為的研究
Efficient Mining of Web Traversal Walks with Site Topology作者 李華富
Lee, Hua-Fu貢獻者 沈錳坤
Shan, Man-Kwan
李華富
Lee, Hua-Fu關鍵詞 網際探勘
使用者網站瀏覽行為探勘
網站漫遊行為
Web Mining
Web Usage Mining
Web Traversal Walk日期 2001 上傳時間 18-Apr-2016 16:32:01 (UTC+8) 摘要 隨著全球資訊網的發展，網站吸引了大量的使用者．分析網站中大部分使用者共同的網站瀏覽行為，不但有助於網站結構的設計與更新，也可以對具有相同瀏覽行為的使用者，做有效的個人化服務。
With progressive expansion in the size and complexity of web site on the World Wide Web, much research has been done on the discovery of useful and interesting Web traversal patterns.
封面頁 證明書 論文摘要 致謝詞 目錄 圖目錄 表目錄 1 Introduction 1.1 Knowledge Discovery in Databases 1.2 Motivation 1.3 Outline of the Thesis 2 Background 2.1 From Data Mining to Web Mining 2.2 Web Usage Mining 2.2.1 Web Log files 2.2.2 Data Preprocessing 2.2.3 Related Work for Web Usage Mining 2.3 Efficient Data Mining for Path Traversal Patterns 2.4 WUM: A Web Utilization Miner 2.5 WAP-mine: Mining Access Patterns Efficiently from Web Logs 2.6 Web Traversal Walk 3 Mining Web Traversal Walks 3.1 Problem Formulation 3.2 An Apriori-based Algorithm for Mining Web Traversal Walks 3.2.1 Extraction of Forward and Backward Traversal Paths 3.2.2 Mining Frequent Forward and Backward Traversal Paths 3.2.3 Mining Frequent Web Traversal Walks 3.2.4 Discovery of Maximal Web Traversal Walks 4 Fast Discovery of Web Traversal Walks 4.1 Using Prefix Property for Web Traversal Walk Mining 4.1.1 Constructing of an Aggregation Tree from Web Transaction Database 4.1.2 Extracting the Prefix Tree and Backward Tree from the Aggregation Tree 4.1.3 Extracting Frequent Web Traversal Walks from Prefix Tree with Backward Tree 5 Experimental Evaluation 5.1 Synthetic Data Generation 5.2 Experimental Results 6 Conclusions and Future Work 6.1 Conclusions 6.2 Future Work References參考文獻 [1] Agrawal, R., Imielinski, T., and A. Swami. (1993). Mining Association Rules between Sets of Items in Large Databases. Proceedings of ACM SIGMOD International Conference on Management of Data, pages 207-216. [2] Agrawal, R., and Srikant, R. (1994). Fast Algorithm for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, pages 478-499. [3] Agrawal, R., and Srikant, R. (1995). Mining Sequential Patterns. Proceedings of the 11th International Conference on Data Engineering, pages 3-14. [4] Aggarwal, C., Wolf, J. L., and Yu, P. S. (1999). Caching on the World Wide Web. IEEE Transaction on Knowledge and Data Engineering, 11(1), 94-107. [5] Borges, J., and Levene, M. (1998). Mining Association Rules in Hypertext Database. Proceeding of the 4th International Conference on Knowledge Discovery and Data Mining, pages 149-153. [6] Chakrabarti, S., Dom, B. E., Gibson, D., Kleingerg, J., Kumar, R., Raghavan, P., Rajagopalan, S., and Tomkins, A. S. (1999). Mining the Link Structure of the World Wide Web. IEEE Computer, 32(8), 60-67. [7] Chen, M. S., Han, J., and Yu, P. S. (1996). Data Mining: An Overview from Database Perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866-883. [8] Chen, M. S., Park, J. S., and Yu, P. S. (1998). Efficient Data Mining for Path Traversal Patterns. IEEE Transactions on Knowledge and Data Engineering, 10(2), 209-221. [9] Cooley, R., Mobasher, B., and Srivastava, J. (1997). Grouping Web Page References into Transactions for Mining World Wide Web Browsing Patterns. Technical Report TR 97-021, Dept. of Computer Science, Univ. of Minnesota, Minneapolis. [10] Cooley, R., Mobasher, B., and Srivastava, J. (1997). Web Mining：Information and Pattern Discovery on the World Wide Web. Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence, pages 558-567. [11] Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., and Slattery, S. (1998). Learning to Extract Symbolic Knowledge from the World Wide Web. Proceedings of the 15th National Conference on Artificial Intelligence, pages 509-516. [12] Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From Data Mining to Knowledge Discovery: An Overview. In Advances in Knowledge Discovery and Data Mining. G. Piatetsky-Shapiro and J. Frawley, editors, AAAI Press, Menlo Park, CA. [13] Fu, Y., Sandhu, K., and Shih, M. (1999). Clustering of Web Users Based on Access Patterns, International Workshop on Web Usage Analysis and User Profiling, pages 144-150. [14] Han, J., Zaiane, O. R., and Fu, Y. (1995). Resource and Knowledge Discovery in Global Information Systems: A Scalable Multiple Layered Database Approach. Proceedings of a Forum on Research and Technology Advances in Digital Libraries, pages 331-336. [15] Inktomi (2000). Web Surpasses One Billion Documents. http://www.inktomi.com /news /press/billion.html. [16] Lee, H. F., and Shan, M. K. (2000). Mining Non-Simple Traversal Paths from Web Access Logs. 2000 Workshop on Internet and Distributed System, pages 266-272. [17] Lin, I. Y., Huang, X. M., and Chen, M. S. (1999). Capturing User Access Patterns for Web Data Mining. Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence, pages 345-348. [18] Lin, X., Liu, L., Zhand, Y., and Zhou, X. (1999). Efficiently Computing Frequent Tree-Like Topology Patterns in a Web Environment. Proceedings of the 31th International Conference on Technology of Object-Oriented Languages and Systems, pages 440-447. [19] Luotonen, A. (1995). The Common Logfile Format. http://www.w3.org/Daemon/User/Config/Logging.html. [20] Massegila, F., Cathala, F., and Poncelet, P. (1998). The Psp Approach for Mining Sequential Patterns. Proceedings of European Symposium on Principle of Data Mining and Knowledge Discovery, pages 176-184. [21] Masseglia, F., Poncelet, P., and Cicchetti, R. (1999). An Efficient Algorithm for Web Usage Mining. Network and Information System Journal, 2(5), 571-603. [22] Mena, J. (1999). Data Mining Your Website. Digital Press. [23] Park, J. S., Chen, M.S., and Yu, P. S. (1998). An Effective Hash Based Algorithm for Mining Association Rules. IEEE Transactions on Knowledge and Data Engineering, 10(2), 813-825. [24] Pazzani, M., Muramatsu, J., and Billsus, D. (1996). Syskill & Webert: Identifying Interesting Web Sites. Proceedings AAAI Spring Symposium: Machine Learning in Information Access, pages 54-61. [25] Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.-C. (2000). FreeSpan: Frequent Pattern-Projected Sequential Pattern Mining. Proceedings of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 355-359. [26] Han, J., Pei, J., and Yin, Y. (2000). Mining Frequent Patterns without Candidate Generation. Proceedings of 2000 ACM-SIGMOD International Conference on Management of Data, pages 1-12. [27] Pei, J., Han, J., Mortazavi-Asl, B., and Zhu, H. (2000). Mining Access Pattern efficiently from Web logs. Proceedings of 2000 Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 396-407. [28] Pei, J., Han, J., Pinto, H., Chen, Q., Dayal, U., and Hsu, M.-C. (2001). PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. Proceedings of 2001 International Conference on Data Engineering, pages 215-224. [29] Pirolli, P., Pitkow, J., and Rao, R. (1996). Silk From a Sow’s Ear: Extracting Usable Structures from the Web. Proceedings of Human Factors in Computing Systems, pages 118-125. [30] Savasere, A., Omiecinski, E., and Navathe, S. (1995). An Effective Algorithm for Mining Association Rules in Large Databases. Proceedings of the 22th International Conference on Very Large Data Bases, pages 432-443. [31] Schechter, S., Krishnan, M., and Smith, M. D. (1998). Using Path Profiles to Predict HTTP Requests. Proceedings of 7th International World Wide Web Conference, pages 457-467. [32] Shahabi, C., Zarkesh, A. M., Adibi, J., and Shah, V. (1997). Knowledge discovery from users Web-page navigation. Proceedings of Workshop on Research Issues in Data Engineering, pages 263-274. [33] Spertus, E. (1997). ParaSite: Mining Structural Information on the Web. Computer Networks and ISDN Systems, 8(13), 1205-1214. [34] Spiliopoulou, M. and Faulstich, L. C. (1998). WUM: A Tool for Web Utilization Analysis. EDBT Workshop WebDB`98, Valencia, Spain, pages 184-203. [35] Spiliopoulou, M., Faulstich, L. C., and K. Winkler, K. (1999). A Data Miner analyzing the Navigational Behaviour of Web Users. Proceedings of the Workshop on Machine Learning in User Modelling of the ACAI`99 International Conference, pages 113-126. [36] Srikant, R., and Agrawal, R. (1996). Mining Sequential Patterns: Generalizations and Performance Improvements. Proceedings of the 5th International Conference on Extending Database Technology, pages 3-17. [37] Wu, K. L., Yu, P. S., and Ballman, A.. (1998). SpeedTracer: A Web Usage Mining and Analysis Tool. IBM System Journal, 37(1), 89-105. [38] Yan, T. W., Jacobsen, M., Hector, G. M., and Dayal, U. (1996). From User Access Patterns to Dynamic Hypertext Linking. Proceedings of 5th International World Wide Web Conference, pages 7-11. [39] Yang, D. L., and Yang, S. H. (2000). An Efficient Web Mining for Session Path Patterns. Proceedings of 2000 International Computer Symposium, pages 107-113. [40] Yun C. H., and Chen, M. S. (2000). Mining Web Transaction Patterns: Capturing Consumer Traveling and Purchasing Patterns in an Electronic Commerce Environment. Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 216-219. [41] Zaiane O. R., and Han, J. (1998). WebML: Querying the World Wide Web for Resources and Knowledge. Proceedings of International Workshop on Web Information and Data Management, pages 331-338. [42] Zaiane, O. R., Xin, M., and Han, J. (1998). Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs. Proceedings of Advances in Digital Libraries Conference, pages 19-29. 描述 碩士
國立政治大學
資訊科學學系資料來源 http://thesis.lib.nccu.edu.tw/record/#A2002001570 資料類型 thesis dc.contributor.advisor 沈錳坤 zh_TW dc.contributor.advisor Shan, Man-Kwan en_US dc.contributor.author (Authors) 李華富 zh_TW dc.contributor.author (Authors) Lee, Hua-Fu en_US dc.creator (作者) 李華富 zh_TW dc.creator (作者) Lee, Hua-Fu en_US dc.date (日期) 2001 en_US dc.date.accessioned 18-Apr-2016 16:32:01 (UTC+8) - dc.date.available 18-Apr-2016 16:32:01 (UTC+8) - dc.date.issued (上傳時間) 18-Apr-2016 16:32:01 (UTC+8) - dc.identifier (Other Identifiers) A2002001570 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/85504 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description.abstract (摘要) 隨著全球資訊網的發展，網站吸引了大量的使用者．分析網站中大部分使用者共同的網站瀏覽行為，不但有助於網站結構的設計與更新，也可以對具有相同瀏覽行為的使用者，做有效的個人化服務。 zh_TW dc.description.abstract (摘要) With progressive expansion in the size and complexity of web site on the World Wide Web, much research has been done on the discovery of useful and interesting Web traversal patterns. en_US dc.description.abstract (摘要) 封面頁 證明書 論文摘要 致謝詞 目錄 圖目錄 表目錄 1 Introduction 1.1 Knowledge Discovery in Databases 1.2 Motivation 1.3 Outline of the Thesis 2 Background 2.1 From Data Mining to Web Mining 2.2 Web Usage Mining 2.2.1 Web Log files 2.2.2 Data Preprocessing 2.2.3 Related Work for Web Usage Mining 2.3 Efficient Data Mining for Path Traversal Patterns 2.4 WUM: A Web Utilization Miner 2.5 WAP-mine: Mining Access Patterns Efficiently from Web Logs 2.6 Web Traversal Walk 3 Mining Web Traversal Walks 3.1 Problem Formulation 3.2 An Apriori-based Algorithm for Mining Web Traversal Walks 3.2.1 Extraction of Forward and Backward Traversal Paths 3.2.2 Mining Frequent Forward and Backward Traversal Paths 3.2.3 Mining Frequent Web Traversal Walks 3.2.4 Discovery of Maximal Web Traversal Walks 4 Fast Discovery of Web Traversal Walks 4.1 Using Prefix Property for Web Traversal Walk Mining 4.1.1 Constructing of an Aggregation Tree from Web Transaction Database 4.1.2 Extracting the Prefix Tree and Backward Tree from the Aggregation Tree 4.1.3 Extracting Frequent Web Traversal Walks from Prefix Tree with Backward Tree 5 Experimental Evaluation 5.1 Synthetic Data Generation 5.2 Experimental Results 6 Conclusions and Future Work 6.1 Conclusions 6.2 Future Work References - dc.description.tableofcontents 封面頁 證明書 論文摘要 致謝詞 目錄 圖目錄 表目錄 1 Introduction 1.1 Knowledge Discovery in Databases 1.2 Motivation 1.3 Outline of the Thesis 2 Background 2.1 From Data Mining to Web Mining 2.2 Web Usage Mining 2.2.1 Web Log files 2.2.2 Data Preprocessing 2.2.3 Related Work for Web Usage Mining 2.3 Efficient Data Mining for Path Traversal Patterns 2.4 WUM: A Web Utilization Miner 2.5 WAP-mine: Mining Access Patterns Efficiently from Web Logs 2.6 Web Traversal Walk 3 Mining Web Traversal Walks 3.1 Problem Formulation 3.2 An Apriori-based Algorithm for Mining Web Traversal Walks 3.2.1 Extraction of Forward and Backward Traversal Paths 3.2.2 Mining Frequent Forward and Backward Traversal Paths 3.2.3 Mining Frequent Web Traversal Walks 3.2.4 Discovery of Maximal Web Traversal Walks 4 Fast Discovery of Web Traversal Walks 4.1 Using Prefix Property for Web Traversal Walk Mining 4.1.1 Constructing of an Aggregation Tree from Web Transaction Database 4.1.2 Extracting the Prefix Tree and Backward Tree from the Aggregation Tree 4.1.3 Extracting Frequent Web Traversal Walks from Prefix Tree with Backward Tree 5 Experimental Evaluation 5.1 Synthetic Data Generation 5.2 Experimental Results 6 Conclusions and Future Work 6.1 Conclusions 6.2 Future Work References zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#A2002001570 en_US dc.subject (關鍵詞) 網際探勘 zh_TW dc.subject (關鍵詞) 使用者網站瀏覽行為探勘 zh_TW dc.subject (關鍵詞) 網站漫遊行為 zh_TW dc.subject (關鍵詞) Web Mining en_US dc.subject (關鍵詞) Web Usage Mining en_US dc.subject (關鍵詞) Web Traversal Walk en_US dc.title (題名) 考慮網站結構之使用者網站漫遊行為的研究 zh_TW dc.title (題名) Efficient Mining of Web Traversal Walks with Site Topology en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Agrawal, R., Imielinski, T., and A. Swami. (1993). Mining Association Rules between Sets of Items in Large Databases. Proceedings of ACM SIGMOD International Conference on Management of Data, pages 207-216. [2] Agrawal, R., and Srikant, R. (1994). Fast Algorithm for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, pages 478-499. [3] Agrawal, R., and Srikant, R. (1995). Mining Sequential Patterns. Proceedings of the 11th International Conference on Data Engineering, pages 3-14. [4] Aggarwal, C., Wolf, J. L., and Yu, P. S. (1999). Caching on the World Wide Web. IEEE Transaction on Knowledge and Data Engineering, 11(1), 94-107. [5] Borges, J., and Levene, M. (1998). Mining Association Rules in Hypertext Database. Proceeding of the 4th International Conference on Knowledge Discovery and Data Mining, pages 149-153. [6] Chakrabarti, S., Dom, B. E., Gibson, D., Kleingerg, J., Kumar, R., Raghavan, P., Rajagopalan, S., and Tomkins, A. S. (1999). Mining the Link Structure of the World Wide Web. IEEE Computer, 32(8), 60-67. [7] Chen, M. S., Han, J., and Yu, P. S. (1996). Data Mining: An Overview from Database Perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866-883. [8] Chen, M. S., Park, J. S., and Yu, P. S. (1998). Efficient Data Mining for Path Traversal Patterns. IEEE Transactions on Knowledge and Data Engineering, 10(2), 209-221. [9] Cooley, R., Mobasher, B., and Srivastava, J. (1997). Grouping Web Page References into Transactions for Mining World Wide Web Browsing Patterns. Technical Report TR 97-021, Dept. of Computer Science, Univ. of Minnesota, Minneapolis. [10] Cooley, R., Mobasher, B., and Srivastava, J. (1997). Web Mining：Information and Pattern Discovery on the World Wide Web. Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence, pages 558-567. [11] Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., and Slattery, S. (1998). Learning to Extract Symbolic Knowledge from the World Wide Web. Proceedings of the 15th National Conference on Artificial Intelligence, pages 509-516. [12] Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From Data Mining to Knowledge Discovery: An Overview. In Advances in Knowledge Discovery and Data Mining. G. Piatetsky-Shapiro and J. Frawley, editors, AAAI Press, Menlo Park, CA. [13] Fu, Y., Sandhu, K., and Shih, M. (1999). Clustering of Web Users Based on Access Patterns, International Workshop on Web Usage Analysis and User Profiling, pages 144-150. [14] Han, J., Zaiane, O. R., and Fu, Y. (1995). Resource and Knowledge Discovery in Global Information Systems: A Scalable Multiple Layered Database Approach. Proceedings of a Forum on Research and Technology Advances in Digital Libraries, pages 331-336. [15] Inktomi (2000). Web Surpasses One Billion Documents. http://www.inktomi.com /news /press/billion.html. [16] Lee, H. F., and Shan, M. K. (2000). Mining Non-Simple Traversal Paths from Web Access Logs. 2000 Workshop on Internet and Distributed System, pages 266-272. [17] Lin, I. Y., Huang, X. M., and Chen, M. S. (1999). Capturing User Access Patterns for Web Data Mining. Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence, pages 345-348. [18] Lin, X., Liu, L., Zhand, Y., and Zhou, X. (1999). Efficiently Computing Frequent Tree-Like Topology Patterns in a Web Environment. Proceedings of the 31th International Conference on Technology of Object-Oriented Languages and Systems, pages 440-447. [19] Luotonen, A. (1995). The Common Logfile Format. http://www.w3.org/Daemon/User/Config/Logging.html. [20] Massegila, F., Cathala, F., and Poncelet, P. (1998). The Psp Approach for Mining Sequential Patterns. Proceedings of European Symposium on Principle of Data Mining and Knowledge Discovery, pages 176-184. [21] Masseglia, F., Poncelet, P., and Cicchetti, R. (1999). An Efficient Algorithm for Web Usage Mining. Network and Information System Journal, 2(5), 571-603. [22] Mena, J. (1999). Data Mining Your Website. Digital Press. [23] Park, J. S., Chen, M.S., and Yu, P. S. (1998). An Effective Hash Based Algorithm for Mining Association Rules. IEEE Transactions on Knowledge and Data Engineering, 10(2), 813-825. [24] Pazzani, M., Muramatsu, J., and Billsus, D. (1996). Syskill & Webert: Identifying Interesting Web Sites. Proceedings AAAI Spring Symposium: Machine Learning in Information Access, pages 54-61. [25] Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.-C. (2000). FreeSpan: Frequent Pattern-Projected Sequential Pattern Mining. Proceedings of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 355-359. [26] Han, J., Pei, J., and Yin, Y. (2000). Mining Frequent Patterns without Candidate Generation. Proceedings of 2000 ACM-SIGMOD International Conference on Management of Data, pages 1-12. [27] Pei, J., Han, J., Mortazavi-Asl, B., and Zhu, H. (2000). Mining Access Pattern efficiently from Web logs. Proceedings of 2000 Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 396-407. [28] Pei, J., Han, J., Pinto, H., Chen, Q., Dayal, U., and Hsu, M.-C. (2001). PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. Proceedings of 2001 International Conference on Data Engineering, pages 215-224. [29] Pirolli, P., Pitkow, J., and Rao, R. (1996). Silk From a Sow’s Ear: Extracting Usable Structures from the Web. Proceedings of Human Factors in Computing Systems, pages 118-125. [30] Savasere, A., Omiecinski, E., and Navathe, S. (1995). An Effective Algorithm for Mining Association Rules in Large Databases. Proceedings of the 22th International Conference on Very Large Data Bases, pages 432-443. [31] Schechter, S., Krishnan, M., and Smith, M. D. (1998). Using Path Profiles to Predict HTTP Requests. Proceedings of 7th International World Wide Web Conference, pages 457-467. [32] Shahabi, C., Zarkesh, A. M., Adibi, J., and Shah, V. (1997). Knowledge discovery from users Web-page navigation. Proceedings of Workshop on Research Issues in Data Engineering, pages 263-274. [33] Spertus, E. (1997). ParaSite: Mining Structural Information on the Web. Computer Networks and ISDN Systems, 8(13), 1205-1214. [34] Spiliopoulou, M. and Faulstich, L. C. (1998). WUM: A Tool for Web Utilization Analysis. EDBT Workshop WebDB`98, Valencia, Spain, pages 184-203. [35] Spiliopoulou, M., Faulstich, L. C., and K. Winkler, K. (1999). A Data Miner analyzing the Navigational Behaviour of Web Users. Proceedings of the Workshop on Machine Learning in User Modelling of the ACAI`99 International Conference, pages 113-126. [36] Srikant, R., and Agrawal, R. (1996). Mining Sequential Patterns: Generalizations and Performance Improvements. Proceedings of the 5th International Conference on Extending Database Technology, pages 3-17. [37] Wu, K. L., Yu, P. S., and Ballman, A.. (1998). SpeedTracer: A Web Usage Mining and Analysis Tool. IBM System Journal, 37(1), 89-105. [38] Yan, T. W., Jacobsen, M., Hector, G. M., and Dayal, U. (1996). From User Access Patterns to Dynamic Hypertext Linking. Proceedings of 5th International World Wide Web Conference, pages 7-11. [39] Yang, D. L., and Yang, S. H. (2000). An Efficient Web Mining for Session Path Patterns. Proceedings of 2000 International Computer Symposium, pages 107-113. [40] Yun C. H., and Chen, M. S. (2000). Mining Web Transaction Patterns: Capturing Consumer Traveling and Purchasing Patterns in an Electronic Commerce Environment. Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 216-219. [41] Zaiane O. R., and Han, J. (1998). WebML: Querying the World Wide Web for Resources and Knowledge. Proceedings of International Workshop on Web Information and Data Management, pages 331-338. [42] Zaiane, O. R., Xin, M., and Han, J. (1998). Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs. Proceedings of Advances in Digital Libraries Conference, pages 19-29. zh_TW
