D. Aha, D. Kibler, and M. Albert, Instance-based learning algorithms, Machine Learning, vol.57, issue.1, pp.37-66, 1991.
DOI : 10.1007/BF00153759
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.635

N. Altman, An introduction to kernel and nearest-neighbor nonparametric regression, The American Statistician, vol.46, issue.3, pp.175-185, 1992.
DOI : 10.1080/00031305.1992.10475879

H. Bunke and B. Messmer, Similarity measures for structured representations, Lecture Notes in Computer Science, vol.837, pp.106-118, 1994.
DOI : 10.1007/3-540-58330-0_80

C. C. Chang and C. J. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.1-2727, 2011.
DOI : 10.1145/1961189.1961199

J. H. Friedman, J. L. Bentley, and R. A. Finkel, An Algorithm for Finding Best Matches in Logarithmic Expected Time, ACM Transactions on Mathematical Software, vol.3, issue.3, pp.209-226, 1977.
DOI : 10.1145/355744.355745

A. Ganapathi, H. Kuno, U. Dayal, J. L. Wiener, A. Fox et al., Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning, 2009 IEEE 25th International Conference on Data Engineering, p.130, 2009.
DOI : 10.1109/ICDE.2009.130

C. Gupta, A. Mehta, and U. Dayal, PQR: Predicting Query Execution Times for Autonomous Workload Management, 2008 International Conference on Autonomic Computing, p.12, 2008.
DOI : 10.1109/ICAC.2008.12

M. Hall, E. Frank, G. Holmes, B. Pfahringer, and P. Reutemann, The WEKA data mining software, ACM SIGKDD Explorations Newsletter, vol.11, issue.1, 2009.
DOI : 10.1145/1656274.1656278

L. Kaufman and P. Rousseeuw, Clustering by means of medoids, Statistical Data Analysis based on the L1 Norm, pp.405-416, 1987.

M. Morsey, J. Lehmann, S. Auer, N. Ngomo, A. C. Aroyo et al., DBpedia SPARQL Benchmark ??? Performance Assessment with Real Queries on Real Data, Lecture Notes in Computer Science, vol.3, issue.3, pp.454-469978, 2011.
DOI : 10.1016/j.websem.2005.06.005

A. Nandi and H. V. Jagadish, Assisted querying using instant-response interfaces, Proceedings of the 2007 ACM SIGMOD international conference on Management of data , SIGMOD '07, 2007.
DOI : 10.1145/1247480.1247640
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.164.6643

D. Pelleg and A. W. Moore, X-means: Extending k-means with efficient estimation of the number of clusters, Proceedings of the Seventeenth International Conference on Machine Learning. pp. 727?734. ICML '00, 2000.

K. Riesen and H. Bunke, Approximate graph edit distance computation by means of bipartite graph matching, Image and Vision Computing, vol.27, issue.7, pp.950-959, 2009.
DOI : 10.1016/j.imavis.2008.04.004

K. Riesen, S. Emmenegger, and H. Bunke, A Novel Software Toolkit for Graph Edit Distance Computation, Lecture Notes in Computer Science, vol.7877, pp.142-151, 2013.
DOI : 10.1007/978-3-642-38221-5_15

S. K. Shevade, S. S. Keerthi, C. Bhattacharyya, and K. R. Murthy, Improvements to the SMO algorithm for SVM regression, IEEE Transactions on Neural Networks, vol.11, issue.5, pp.1188-1193, 2000.
DOI : 10.1109/72.870050

N. Stojanovic and L. Stojanovic, A logic-based approach for query refinement in ontologybased information retrieval s, Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, pp.450-45713, 2004.

G. Zenz, X. Zhou, E. Minack, W. Siberski, and W. Nejdl, From keywords to semantic queries???Incremental query construction on the semantic web, Web Semantics: Science, Services and Agents on the World Wide Web, vol.7, issue.3, pp.166-176, 2009.
DOI : 10.1016/j.websem.2009.07.005