Bernstein, A., Clearwater, S., Hill, S., Perlich, C., & Provost, F. (2002). Discovering knowledge from relational data extracted from business news. In *Proceedings of the Workshop on Multi-Relational Data Mining at KDD-2002* (pp. 7–20). University of Alberta, Edmonton, Canada.

Blockeel, H., & Raedt, L.D. (1998). Top-down induction of first-order logical decision trees.

*Artificial Intelligence*,

*101*, 285–297.

MATHMathSciNetCrossRefBradley, A. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms.

*Pattern Recognition*,

*30:7*, 1145–1159.

CrossRefBrazdil, P., Gama, J., & Henery, R. (1994). Characterizing the applicability of classification algorithms using meta level learning. In *Proceedings of the 7th European Conference on Machine Learning* (pp. 83–102).

Chakrabarti, S., Dom, B., & Indyk, P. (1998). Enhanced hypertext categorization using hyperlinks. In *Proceedings of the International Conference on Management of Data* (pp. 307–318).

Cortes, C., Pregibon, D., & Volinsky, C. (2002). Communities of interest. *Intelligent Data Analysis, 6:3*, 211–219.

Craven, M., & Slattery, S. (2001). Relational learning with statistical predicate invention: Better models for hypertext.

*Machine Learning*,

*43*, 97–119.

MATHCrossRefDerSimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials.

*Controlled Clinical Trials*,

*7*, 177–188.

CrossRefDomingos, P., & Richardson, M. (2001). Mining the network value of customers. In *Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining* (pp. 57–66).

Fawcett, T., & Provost, F. (1997). Adaptive fraud detection.

*Data Mining and Knowledge Discovery*,

*1*, 291–316.

CrossRefFlach, P., & Lachiche, N. (2004). Naive Bayesian classification for structured data. In *Machine Learning*, *57*, 233–269.

Gärtner, T. (2003). A survey of kernels for structured data. *SIGKDD Explorations*, *5*, 49–58.

Gärtner, T., Lloyd, J.W., & Flach, P.A. (2002). Kernels for structured data. In *Proceedings of the 12th International Conference on Inductive Logic Programming* (pp. 66–83). Springer.

Goldberg, H., & Senator, T. (1995). Restructuring databases for knowledge discovery by consolidation and link formation. In *Proceedings of the 1st International Conference On Knowledge Discovery and Data Mining* (pp. 136–141). Montreal, Canada: AAAI Press.

Jensen, D., & Getoor, L. (2003). In *Proceedings of the Workshop on Learning Statistical Models from Relational Data at IJCAI-2003*. American Association for Artificial Intelligence.

Jensen, D., & Neville, J. (2002). Linkage and autocorrelation cause feature selection bias in relational learning. In *Proceedings of the 19th International Conference on Machine Learning* (pp. 259–266). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.

Jensen, D., Neville, J., & Gallagher, B. (2004). Why collective inference improves relational classification. In *Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining* (pp. 593–598). New York, NY, USA: ACM Press.

Jensen, D., Neville, J., & Hay, M. (2003). Avoiding bias when aggregating relational data with degree disparity. In *Proceedings of the 20th International Conference on Machine Learning* (pp. 274–281).

Kietz, J.-U., & Morik, K. (1994). A polynomial approach to the constructive induction of structural knowledge.

*Machine Learning*,

*14*, 193–217.

MATHCrossRefKirsten, M., Wrobel, S., & Horvath, T. (2000). Distance based approaches to relational learning and clustering. In S. Ďzeroski & N.Lavrač (Eds.), *Relational data mining*, (pp. 213–232). Springer Verlag.

Knobbe, A., Haas, M.D., & Siebes, A. (2001). Propositionalisation and aggregates. In *Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery* (pp. 277–288).

Koller, D., & Pfeffer, A. (1998). Probabilistic frame-based systems. In *Proceedings of the 15th National Conference on Artificial Intelligence (AAAI)* (pp. 580–587).

Kramer, S., Lavrač, N., & Flach, P. (2001). Propositionalization approaches to relational data mining. In S. Ďzeroski and N. Lavrač (Eds.), *Relational data mining* (pp. 262–291). Springer-Verlag.

Krogel, M.-A., Rawles, S., Železng, F., Flach, P., Lavrač, N., & Wrobel, S. (2003). Comparative evaluation of approaches to propositionalization. In *13th International Conference on Inductive Logic Programming (ILP)* (pp. 197–214).

Krogel, M.-A., & Wrobel, S. (2001). Transformation-based learning using multirelational aggregation. In *Proceedings of the 11th International Conference on Inductive Logic Programming (ILP)* (pp. 142–155).

Krogel, M.-A., & Wrobel, S. (2003). Facets of aggregation approaches to propositionalization. In *Proceedings of the 13th International Conference on Inductive Logic Programming (ILP)* (pp. 30–39).

Lavrač, N., & Ďzeroski, S. (1994). *Inductive logic programming: techniques and application*. New York. Ellis Horwood

Libkin, L., & Wong L. (1994). New techniques for studying set languages, bag languages and aggregate functions. In *Proceedings of the 13th Symposium on Principles of Database Systems* (pp. 155–166).

Macskassy, S., & Provost, F. (2003). A simple relational classifier. In *Proceedings of the Workshop on Multi-Relational Data Mining at KDD-2003*.

Macskassy, S., & Provost, F. (2004). *Classification in networked Data: A Toolkit and a Univariate Case Study* (Technical Report CeDER-04-08). Stern School of Business, New York University.

McCallum, A., Nigam, K., J. Rennie, & Seymore, K. (2000). Automating the construction of internet portals with machine learning.

*Information Retrival*,

*3*, 127–163.

CrossRefMcCreath, E. (1999). *Induction in First Order Logic from Noisy Training Examples and Fixed Example Set Size*. Doctoral dissertation, Universtity of New South Wales.

Michalski, R. (1983). A theory and methodology of inductive learning.

*Artificial Intelligence, 20*, 111–161.

MathSciNetCrossRefMorik, K. (1999). Tailoring representations to different requirements. In *Proceedings of the 10th International Conference on Algorithmic Learning Theory (ALT)* (pp. 1–12).

Muggleton, S. (2001). CProgol4.4: A tutorial introduction. In S. Ďzeroski & N.Lavrač (Eds.), *Relational Data Mining* pp.(105–139). Springer-Verlag.

Muggleton, S., & DeRaedt, L. (1994). Inductive logic programming: Theory and methods.

*The Journal of Logic Programming, 19 & 20*, 629–680.

MATHMathSciNetCrossRefNeville, J., & Jensen, D. (2005). Leveraging relational autocorrelation with latent group models. In *Proceedings of the 5th IEEE International Conference on Data Mining* (pp. 49–55). New York, NY, USA: ACM Press.

Neville, J., Jensen, D., Friedland, L., & Hay, M. (2003a). Learning relational probability trees. In *Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining* (pp. 625–630). New York, NY, USA: ACM Press.

Neville, J., Jensen, D., & Gallagher, B. (2003b). Simple estimators for relational Bayesian classifers. In *Proceedings of the 3rd International Conference on Data Mining* (pp. 609–612). Washington, DC, USA: IEEE Computer Society.

Neville, J., Rattigan, M., & Jensen, D. (2003c). Statistical relational learning: Four claims and a survey. In *Proceedings of the Workshop on Learning Statistical Models from Relational Data at IJCAI-2003*.

Özsoyoǵlu, G., Özsoyoǵlu, Z., & Matos, V. (1987). Extending relational algebra and relational calculus with set-valued atributes and aggregate functions.

*ACM Transactions on Database Systems*,

*12*, 566–592.

CrossRefPerlich, C. (2005a). Approaching the ILP challenge 2005: Class-conditional Bayesian propositionalization for genetic classification. In *Late-Braking track at the 15th International Conference on Inductive Logic Programming* (pp. 99–104).

Perlich, C. (2005b). *Probability estimation in mulit-relational domain*. Doctoral dissertation, Stern School of Business.

Perlich, C., & Provost, F. (2003). Aggregation-based feature invention and relational concept classes. In *Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining* (pp. 167–176). New York, NY, USA: ACM Press.

Perlich, C., Provost, F., & Simonoff, J. (2003). Tree induction vs. logistic regression: A learning-curve analysis.

*Journal of Machine Learning Research*,

*4*, 211–255.

MathSciNetCrossRefPompe, U., & Kononenko, I. (1995). Naive Bayesian classifier with ILP-R. In *Proceedings of the 5th International Workshop on Inductive Logic Programming* (pp. 417–436).

Popescul, A., & Ungar, L. (2003). Structural logistic regression for link analysis. In *Proceedings of the Workshop on Multi-Relational Data Mining at KDD-2003*.

Popescul, A., & Ungar, L. (2004). Cluster-based concept invention for statistical relational learning. In *Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining* (pp. 665–670).

Popescul, A., Ungar, L., Lawrence, S., & Pennock, D.M. (2002). Structural logistic regression: Combining relational and statistical learning. In *Proceedings of the Workshop on Multi-Relational Data Mining at KDD-2003* (pp. 130–141).

Provost, F., Perlich, C., & Macskassy, S. (2003). Relational learning problems and simple models. In *Proceedings of the Workshop on Learning Statistical Models from Relational Data at IJCAI-2003* (pp. 116–120).

Quinlan, J. (1993). *C4.5: Programs for machine learning*. Los Altos, California: Morgan Kaufmann Publishers.

Quinlan, J., & Cameron-Jones, R. (1993). FOIL: A midterm report. In *Proceedings of the 6th European Conference on Machine Learning (ECML)* (pp. 3–20).

Slattery, S., & Mitchell, T. (2000). Discovering test set regularities in relational domains. In *Proceedings of the 17th International Conference on Machine Learning* (pp. 895–902).

Taskar, B., Abbeel, P., & Koller, D. (2002). Discriminative probabilistic models for relational data. In *Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence* (pp. 485–492). Edmonton, Canada: Morgan Kaufmann.

Taskar, B., Segal, E., & Koller, D. (2001). Probabilistic classification and clustering in relational data. In *Proceedings of the 17th International Joint Conference on Artificial Intelligence* (pp. 870–878).

Witten, I., & Frank, E. (1999). *Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations*. Morgan Kaufmann.

Wnek, J., & Michalski, R. (1993). Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments. *Machine Learning*, *14*, 139–168.

Woznica, A., Kalousis, A., & Hilario, M. (2004). Kernel-based distances for relational learning. In *Proceedings of the Workshop on Multi-Relational Data Mining at KDD-2004*.

Zheng, Z., Kohavi, R., & Mason, L. (2001). Real World Performance of Association Rule Algorithms. In *Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining* (pp. 401–406).