網頁探勘(Web Usage Mining)是一種分析使用者瀏覽行為的方法，本論文使用歸納邏輯程式(Inductive Logic Programming)技術來探勘動態網頁。既然動態網頁是由CGI程式加上參數產生，而這些參數間存有某種關連情況下可以在歸納邏輯程式中轉換為背景知識。歸納邏輯程式的正面與負面例子資料部分，則從網路日誌與使用者個人檔案中收集與分類而得。有了背景知識與例子，歸納邏輯程式可以自動地產生瀏覽行為的規則。使用歸納邏輯程式進行網頁探勘的好處有：(1)可以結構化與易於瞭解的形式呈現與背景知識有關的網頁與使用者。(2)歸納邏輯程式的結果是合理的與能夠解釋使用者行為。本論文的目的是瞭解網頁客製化問題與減少動態網頁產生的頻寬。

Web usage mining is a means to analyze the browsing behaviors of users on the Internet. This research employs the techniques of inductive logic programming (ILP) to explore web usage mining with dynamic web pages. Since dynamic web pages are generated by executing CGI-programs with a series of parameters, the relationships among the parameters can be viewed as the background knowledge to ILP. We collect from the web logs and user profiles and classify them into positive and negative examples as inputs to ILP accordingly. Hypotheses of the browsing patterns are automatically generated by ILP. The benefits of using ILP on web usage mining include (1) readable and structured representation of the background knowledge about the web pages and users; (2) reasonable and explainable conclusions supported by inductive learning theories. In this thesis, we target on identifying the customization problem of web pages and the reduction of communication bandwidth caused by dynamic web pages. Experimental results show that ILP can serve as flexible platform for web usage mining.

第一章
1.1 網頁呈現 1
1.2 網頁探勘 4
1.3 研究動機 5
1.4 論文結構 7
第二章 相關文獻
2.1 網頁探勘 8
2.2 網站個人化 10
第三章 歸納邏輯程式
3.1 定義 13
3.2 親屬關係的例子 15
3.3 相關文獻與應用 19
第四章 問題描述
4.1 動態網頁 27
4.2 動態網頁與歸納邏輯程式的對應關係 31
4.3 問題呈現 31
4.4 定義問題 36
4.5 一般性網頁探勘架構 38
第五章 系統設計與實驗
5.1 定義問題 42
5.2 資料前置處理 43
5.3 轉換至邏輯子句 46
5.4 選擇背景知識 46
5.5 選擇例子 52
5.6 實驗結果 53
5.7 驗證規則 57
第六章 結論
6.1 討論 61
6.2 未來發展 53
參考書目 64

[1] Cooley, R., Tan, P. N., Srivastava, J. (1999), Discovery of Interesting Usage Pattern from Web Data. Technical Report TR99-022, University of Minnesota.
[2] (PHP.net) http://www.php.net/
[3] (PHP-Nuke) http://www.phpnuke.org/
[4] Cooley, R., Tan, Pang-Ning., and Srivastava, J. (1999), WebSIFT, The Web Site Information Filter System, In Proceedings of the 1999 KDD Workshop on Web Mining.
[5] Cooley, R., Tan, Pang-Ning., and Srivastava, J. (1999), Data Preparation for Mining World Wide Web Browsing Patterns, Knowledge and Information Systems, Vol. 1, No. 1, pp. 5-32.
[6] Han, J., Pei. J, and Yin. Y. (2000), Mining Frequent Patterns Without Candidate Generation, 2000 ACM SIGMOD International Conference on Management of Data, pp.1-12.
[7] Pei, J., Han, J., Mortazavi-asl, B., and Zhu, H. (2000), Mining Access Patterns Efficiently from Web Logs, In: Pacific-Asia Conference on Knowledge Discovery and Data Mining.
[8] Spiliopoulou, M., Faulstich, L.C., and Winkler, K. (1999), A Data Miner Analyzing the Navigational Behavior of Web Users, Principles of Data Mining and Knowledge Discovery, pp. 588-589.
[9] Spiliopoulou, M. (2000), Web Usage Mining for Web Site Evaluation, In Communications of the ACM, Vol.43, No. 8, pp. 127-134.
[10] Pazzani, M. (1999), A Framework for Collaborative, Content-Based and Demographic Filtering, Artificial Intelligence Review, Vol.13, No.5-6, pp.393-408.
[11] Oard, D. W. and Kim, J. (1998), Implicit Feedback for Recommender Systems, In Proceedings of the AAAI Workshop on Recommender Systems, pp.81-83.
[12] Goldberg, D., Nichols, D., Oki, B., and Terry, D. (1992), Using Collaborative Filtering to Weave an Information Tapestry, Communications of the ACM, Vol.35, No.12, pp.61-69.
[13] Lang, K. (1995), NewsWeeder, Learning to Filter Netnews, In Proceedings of the 12th International Conference on Machine Learning.
[14] Billsus, D. and Pazzani, M.J. (1999), A Hybrid User Model for News Story Classification, In Proceedings of the 7th International Conference, pp.99-108
[15] Sheth, B. and Maes, P. (1993), Evolving Agents for Personalized Information Filtering, In Proceedings of the 9th Conference on AI for Applications, IEEE Computer Society Press.
[16] Aggarwal, C.C. and Yu, P.S. (2000), Data Mining Techniques for Personalization, IEEE Data Engineering Bulletin, Vol. 23, pp. 4-9.
[17] Fu, X., Budzik, J. and Hammond, K.J. (2000), Mining navigation history for recommendation. In Proceedings of 2000 International Conference Intelligent User Interfaces, Vol.106-112.
[18] Wang, J., Chen, Z., Tao, L., Ma, W.Y., and Wenyin, L. (2002), Ranking User's Relevance to A Topic Through Link Analysis on Web Logs, In Proceedings 4th ACM CIKM Nternational Workshop on Web Information and Data Management, pp. 49-54
[19] (Alexa) http://www.alexa.com
[20] Miller. D. and Nadathur, G. (1998), An Overview of Prolog. In Proceedings of the 5th International Logic Programming Conference 5th Symposium on Logic Programming, MIT Press, pp. 810-827.
[21] Overview of Prolog Systems : http://www.complang.tuwien.ac.at/ulrich/prolog_misc/systems
[22] Lavrac, N. and Dzeroski, S. (1994), Inductive logic programming: techniques and applications, Ellis Horwood, pp.26.
[23] Muggleton, S. (1992), Inductive logic programming, Academic Press.
[24] Lavrac, N. and Dzeroski, S. (1994), Inductive logic programming: techniques and applications, Ellis Horwood.
[25] De Raedt, L. (1996), Advances in Inductive Logic Programming, IOS Press.
[26] ILPnet (The INCO program), http://www-ai.ijs.si/ilpnet/
[27] ILPnet2 (The INCO program), http://www-ai.ijs.si/~ilpnet2/
[28] Russell, S. and Norvig, P. (2003), Artificial Intelligence: A Modern Approach, Prentice Hall Series in Artificial Intelligence, New Jersey.
[29] Shapiro, E. Y. (1983), Algorithmic Program Debugging, Cambridge, MA, MIT Press.
[30] Bergadano, F. and Gunetti, D. (1995), Inductive Logic Programming: from Machine Learning to Software Engineering, MIT Press, pp. 79.
[31] Lavrac, N. and Dzeroski, S. (1994), Inductive Logic Programming: Techniques and Applications, Ellis Horwood, pp.137.
[32] Quinlan, J.R. and Cameron-Jones, R.M. (1993), FOIL: A Midterm Report, In Proceedings of the 6th European Conference on Machine Learning, Vol. 667 of Lecture Notes in Artificial Intelligence, pp.3-20, Springer-Verlag.
[33] Dzeroski, S. (1993), Handling Imperfect Data in Inductive Logic Programming, In Proceedings of the 4th Scandinavian Conference on Artificial Intelligence, pp.111-125, IOS Press.
[34] Bergadano, F., Giordana, A., and Saitta, L. (1988), Automated Concept Acquisition in Noisy Environments, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.10, No.4, pp.555-578.
[35] Pazzani, M. J. and Kibler, D. (1992), The utility of knowledge in inductive learning. Machine Learning, Vol.9, No.1, pp.57-94.
[36] Shapiro, E. (1983), Algorithmic program debugging, MIT Press.
[37] Ferri-Ramírez, C., Hernández-Orallo, J., and Ramírez-Quintana, M.J. (2000), FLIP: User's Manual, (v0.7).
[38] Lavrac, N. and Dzeroski, S. (1994), Inductive Logic Programming: Tchniques and applications, Ellis Horwood.
[39] Muggleton, S. and Buntine, W. (1992), Machine Invention of First-order Predicates by Inverting Resolution, In Muggleton, S., editor. Inductive Logic Programming, pp.261-280, Academic Press.
[40] Muggleton, S. and Buntine, W. (1988), Machine Invention of First Order Predicates by Inverting Resolution, In Proceedings of the 5th Conference on Machine Learning, pp.339-352, San Mateo, CA.
[41] Lapointe, S. and Matwin, S. (1992), Sub-unification: A Tool for Efficient Induction of Recursive Programs, In Proceedings of 9th International Workshop on Machine Learning, pp.273-28.
[42] Aha, D.W., Lapointe, S., Ling, C.X., and Matwin, S. (1994), Inverting Implication with Small Training Sets, In Proceedings of the 7th European Conference on Machine Learning, Vol.784, pp.31-48.
[43] Muggleton, S. (1995), Inverse Entailment and Progol, New Generation Computing. Specia l issue on Inductive Logic Programming, Vol.13, No.3-4, pp.245-286.
[44] Muggleton, S. (1996), Learning from Positive Data, In Proceedings of the 6th International Workshop on Inductive Logic Programming, Vol.1314 of Lecture Notes in Artificial Intelligence, pp.358-376, Springer-Verlag.
[45] Roberts, S., Van Laer, W., Jacobs, N., Muggleton, S., and Broughton, J. (1998), A Comparison of ILP and Propositional Systems on Propositional Traffic Data, pp.291-300.
[46] Gunetti, D. and Ruffo, G. (1999), Intrusion Detection through Behavioral Data in 3rd Symposium on Intelligent Data Analysis, LNCS, Springer-Verlag, pp. 268-273.
[47] Karalic, A. and Bratko, I. (1997), First Order Regression, Machine Learning, Vol.26.
[48] Karalic, A., Komel, I., and Posel, R. (1996), Applications of Artificial Intelligence in Mechanical Engineering, In Proceedings of the 5th Electrotechnical and Computer Science Conference, pp.175-178, Portoroz, Slovenia.
[49] Dolsak, B., Bratko, I., and Jezernik, A. (1997), Application of Machine Learning in Finite Element Computation, Machine Learning, Data Mining and Knowledge Discovery: Methods and Applications, John Wiley and Sons.
[50] Popelynsky, L. (1998), Knowledge Discovery in Spatial Data by Means of ILP. In Principles of Data Mining and Knowledge Discovery, In Proceedings of 2nd European Symposium, LNCS 1510, Springer-Verlag, Nantes France. Vol. 1510, pp. 271-279.
[51] Dzeroski, S. and Lavrac, N. (1996), Rule Induction and Instance-Based Learning Applied in Medical Diagnosis, Technology and Health Care, Vol.4, pp.203-221, Elsevier.
[52] Brockhausen, P. (1999), Learning First Order Rules in Intensive Care Monitoring, Late-Breaking, Session held at the 9th International Workshop On Inductive Logic Programming.
[53] Morik, K., Brockhausen, P., and Joachims, T. (1999), Combining Statistical Learning with Aknowledge-Based Approach - A Case Study in Intensive Care Monitoring, In Proceedings of the 16th International Conference on Machine Learning, pp.268-277, Morgan Kaufmann, San Francisco, CA.
[54] Van Laer, W., De Raedt, L., and Dzeroski, S. (1997), On Multi-Class Problems and Discretization in Inductive Logic Programming, In Proceedings Tenth International Symposium on Foundations of Intelligent Systems, pp.277-286, Springer, Berlin.
[55] Dzeroski, S., Blockeel, H., Kompare, B., Kramer, S., Pfahringer, B., and Van Laer, W. (1999), Experiments in Predicting Biodegradability, In Proceedings Ninth International Conference on Inductive Logic Programming, pp.80-91, Springer, Berlin.
[56] Blockeel, H., Dzeroski, S., and Grbovic, J. (1999), Simultaneous Prediction of Multiple Chemical Parameters of River Water Quality with TILDE, In Proceedings Third European Conference on Principles of Data Mining and Knowledge Discovery, pp.15-18, Springer, Berlin.
[57] Dzeroski, S., Grbovic, J., and Demsar, D. (2000), Predicting Chemical Parameters of River Water Quality from Bioindicator Data, Applied Intelligence, Vol.13. No.1, pp.7-17.
[58] Mozetic and Hodoscek., M. (1997), Symbolic Protein Data Base, Technical Report, Jozef Stefan Institute.
[59] Dzeroski. S., Schulze-Kremer, S., Heidtke, K., Siems, K., Wettschereck, D., and Blockeel, H. (1998), Diterpene Structure Elucidation from 13 C NMR Spectra with Inductive Logic Programming, Applied Artificial Intelligence.
[60] Horváth, T., Wrobel, S., and Bohnebeck, U. (2001), Relational Instance-Based Learning with Lists and Terms, Machine Learning, Vol.43 (1/2), pp.53-80.
[61] King, R.D., Muggleton, S., Srinivasan, A., and Sternberg, M.J.E. (1996), Structure-Activity Relationships Derived by Machine Learning, The Use of Atoms and Their Bond Connectivities to Predict Mutagenicity by Inductive Logic Programming, In Proceedings of the National Academy of Sciences, Vol.93, pp.438-442.
[62] Sebag, M. and Rouveirol, C. (1996), Polynomial-time Learning in Logic Programming and Constraint Logic Programming, In Proceedings of the 6th International Workshop on Inductive Logic Programming, pp 105-126.
[63] Muggleton, S., Page, C.D., and Srinivasan, A. (1996), An Initial Experiment into Stereochemistry-Based Drug Design Using ILP, In Proceedings of the 6th International Workshop on Inductive Logic Programming, pp.245-261.
[64] Holbrook, S.R., Muskal, S.M., and Kim, S.H. (1990), Predicting Surface Exposure of Amino Acids from Protein Sequence, Protein Engineering. Vol.3, pp.289-294.
[65] Turcotte, M., Muggleton, S.H., and Sternberg, M.J.E. (1998), Application of ILP to Discover Rules Governing the Three-Dimensional Topology of Protein Structure, In Proceedings of the 8th International Workshop on Inductive Logic Programming, LNAI 1446, pp.53-64, Berlin, Springer-Verlag.
[66] King, R.D. and Srinivasan, A. (1996), Prediction of Rodent Carcinogenicity Bioassays from Molecular Structure Using Inductive Logic Programming, Environmental Health Perspectives, Vol.104, No.5, pp.1031-1040.
[67] Bergadano, F., Gunetti, D., Neri, F., and Ruffo, G. (1997), ILP Data Analysis in Adaptive System and Network Management, Deliverable TO1b, ILP-II (second year).
[68] Bergadano, F. and Ruffo, G. (1997), Preliminary Report on The Relic Learning System. Deliverable TO1a, ILP-II (second year).
[69] Neri, F. and Saitta, L. (1997), Exploring the Power of Genetic Search in Learning Symbolic Classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[70] Bryant, C.H., Srinivasan, A., Whittaker, A., Topp, C., Rawlings, C., and Muggleton, S.H. (2001), Are Grammatical Representations Useful for Learning from Biological Sequence data? - A Case Study. Journal of Computational Biology, Vol.8, No. 5, pp.493-522.
[71] Srinivasan, A. and King, R. D. (1996), Feature Construction with Inductive Logic Programming, A study of Quantitative Predictions of Biological Activity Aided by Structural Attributes, In Proceedings of the 6th International Workshop on Inductive Logic Programming, pp.352-367. Stockholm University, Royal Institute of Technology.
[72] Eohnebeck, U., Salter, W., Horvith, T., Wrobel, S., and Elohm, D. (1998), Measuring Similarity of RNA Structures by Relational Instance-Based Learning, A First Step toward Detecting RNA Signal Structures in Silico, In Proceedings of German Conference on Bioinformatics, Technical Report of University Cologne.
[73] Flener, P. and Yilmaz, S. (1999), Inductive Synthesis of Recursive Logic Programs, Achievements and Prospects, Journal of Logic Programming, Vol.41, No.2-3, pp.141-195.
[74] Mofizur, C.R. and Numao, M. (1996), Learning Simple Recursive Concepts by Discovering Missing Examples, Pacific Rim International Conference on Artificial Intelligence, pp.360-371.
[75] Muggleton, S. (1999) Scientific Knowledge Discovery Using Inductive Logic Programming. CACM 99. Vol. 42, No. 11, pp. 42-46.
[76] Doncescu, A., WAISSMAN, J., RICHARD, G., and ROUX, G. (2002), Characterization of Bio-chemical Signals by Inductive Logic Programming, Knowledge-based System, Vol.15, pp.129-137.
[77] Dzeroski, S., Blockeel, H., Kompare, B., Kramer, S., Pfahringer, B., and Van Laer, W. (1999), Experiments in Predicting Biodegradability, In Proceedings Ninth International Conference on Inductive Logic Programming, pp.80-91, Springer, Berlin.
[78] Quiniou, R., Cordier, M. O., Carrault, G., and Wang, F. (2001), Application of ILP to Cardiac Arrhythmia Characterization for Chronicle Recognition. In Proceedings of the 11th International Conference on Inductive Logic Programming, Vol.2157 of Lecture Notes in Artificial Intelligence, pp.220-227, Springer-Verlag.
[79] Brockhausen, P. (1999), Learning First Order Rules in Intensive Care Monitoring, Late-Breaking, Session held at the Ninth International Workshop On Inductive Logic Programming.
[80] Zelezny, F., Miksovsky, P., Stepankova, O., and Zidek, J. (2000), ILP for Automated Telephony, Work-in-Progress Report of the 10th Inductive Logic Programming Conference, Vol. 1, pp. 276-286.
[81] Tveit, A. (2000), Web Mining with ILP. Norwegian University of Science and Technology.
[82] Dzeroski, S., Jacobs, N., Molina, M., and Moure, C. (1998a), ILP Experiments in Detecting Traffic Problems, In Proc, Tenth European Conference on Machine Learning, pp.61-66. Springer, Berlin.
[83] Dzeroski, S., Jacobs, N., Molina, M., and Moure, C., Muggleton, S., and Van Laer, W. (1998b), Detecting traffic problems with ILP, In Proceedings 8th International Conference on Inductive Logic Programming, pp.281-290, Springer Berlin.
[84] Cuena, J., Hernandez, J., and Molina, M. (1995), Knowledge-Based Models for Adaptive Traffic Management Systems, Transportation Research: Part C, Vol.3, No.5, pp.311-337.
[85] Cohen, W. W. (1994), Recovering Software Specifications with ILP, In Proceedings of the 12th National Conference on Artificial Intelligence, pp.142-148, Morgan Kaufmann.
[86] Bergadano, F. and Gunetti, D. (1995), Inductive Logic Programming, From Machine Learning to Software Engineering, MIT Press.
[87] Hayes, I.I., (1986), Specification Directed Module Testing, IEEE Transactions on Software Engineering, Vol.12, No.1, pp.124-133.
[88] Choqut, N (1986), Test Data Generation Using Prolog with Constraints, In Proceedings of Workshop on Software Test, pp.132-141, Los Alamitos.
[89] Bergadano, F. (1993), Test Case Generation by Means of Learning Techniques, In Proceedings of the ACM-SIGSOFT Conference, pp. 149-162.
[90] Bergadano, F., Brussoti, S., Gunetti, D., and Trinchero, U. (1993), Inductive test case generation. In Proceedings of the 3rd International Workshop on Inductive Logic Programming, pp.11-24.
[91] Cherniavsky, J. C. and Smith, C. H. (1987), A recursion Theoretic Approach to Program Testing, IEEE Transactions on Software Engineering, Vol.13, No.7, pp.777-784.
[92] Horváth, T., Gyimóthy, T., Alexin, Z., and Kocsis F. (1993), Interactive Diagnosis and Testing of Logic Programs, In Proceedings of the 3rd Finnish-Estonian-Hungarian Symposium on Programming Languages and Software Tools Kääriku, Estonia, pp.34-46.
[93] Kókai, G., Harmath, L., and Gyimóthy, T. (1997), IDTS, a Tool for Debugging and Testing of Prolog Programs, The 8th Conference on Logic and Computer Science, pp.103-110.
[94] Harada, M. et al (2000), RuleBase Revision System Theres Using Progol, In Proceedings of Artificial Intelligence and Soft Computing, pp.208-216.
[95] Eirinaki, M. and Vazirgiannis, M. (2003), Web Mining for Web Personalization, ACM Transactions on Internet Technology, Vol.3, No.1, pp.1-27.
[96] Kobsa, A., Koenemann, J., and Pohl, W. (2001), Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships, The Knowledge Engineering Review, Vol.16, No.2, pp.111-155.
[97] Pazzani, M. (1999), A Framework for Collaborative, Content-Based and Demographic Filtering, Artificial Intelligence Review, Vol.13, No.5-6, pp.393-408.
[98] Oard, D. W. and Kim, J. (1998), Implicit Feedback for Recommender Systems, In Proceedings of the AAAI Workshop on Recommender Systems, pp.81-83.
[99] Goldberg, D., Nichols, D., Oki, B., and Terry, D. (1992), Using Collaborative Filtering to Weave an Information Tapestry, Communications of the ACM, Vol.35, No.12, pp.61-69.
[100] Lang, K. (1995), NewsWeeder, Learning to Filter Netnews, In Proceedings of the 12th International Conference on Machine Learning.
[101] Billsus, D. and Pazzani, M.J. (1999), A Hybrid User Model for News Story Classification, In Proceedings of the 7th International Conference, pp.99-108
[102] Sheth, B. and Maes, P. (1993), Evolving Agents for Personalized Information Filtering, In Proceedings of the 9th Conference on AI for Applications, IEEE Computer Society Press.
[103] Shardanand, U. and Maes, P. (1995), Social Information Filtering, Algorithms for Automating ‘‘word of mouth’’, In Proceedings of the ACM Conference on Human Factors and Computing Systems, Vol. 1, pp. 210-217.
[104] Starr, B., Ackerman, M., and Pazzani M. (1996), Do-I-Care, A Collaborative Web Agent, In Proceedings of the ACM Conference on Human Factors in Computing Systems, pp.273-274.
[105] Balabanovic, M. and Shoham, Y. (1997), Fab: Content-Based, Collaborative Recommendation, Communications of the ACM, Vol. 40, Issue.3, pp.66—72.
[106] Alspector, J., Kolcz, A., and Karunanithi, N. (1998), Comparing Feature-Based and Clique-Based User Models for Movie Selection, In Proceedings of the 3rd ACM Conference on Digital Libraries, pp. 11-18.
[107] Konstan, J. A., Miller, B., Maltz, D., Herlocker, J., Gordon, L., and Riedl, J. (1997), GroupLens, Applying Collaborative Filtering to Usenet News, Communications of the ACM, Vol.40, Issue.3, pp.77- 87.
[108] Lee, M. L., Ling, T. W., and Low W. L. (2000), A Knowledge-Based Intelligent Data Cleaner, In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 290-294.
[109] Vassiliadis, P., Vagena, Z., Skiadopoulos, S., Karayannidis, N., and Sellis, T. (2000), Arktos: A Tool for Data Cleaning and Transformation in Data Warehouse Environments, IEEE Data Engineering Bulletin Vol. 23, No. 4, pp. 42-47.
[110] Galhardas, H., Florescu, D., Shasha, D., and Simon., E. (1999), Ajax: An Extensible Data Cleaning Tool, In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 16-18.
[111] Hernandez, M. and Stolfo, S. (1998), Real-World Data is Dirty: Data Cleansing and the Merge/Purge Problem, Journal of Data Mining and Knowledge Discovery, Vol.2, No.1, pp.9—37.
[112] Muggleton, S. and Firth, J. (1997), CProgol4.4: A Tutorial Introduction, ftp://ftp.cs.york.ac.uk/pub/ML\_GROUP/Papers/progtuttheo.ps.gz