Towards Industrial-Like Random SAT Instances
暂无分享,去创建一个
[1] Gilles Dequen,et al. A backbone-search heuristic for efficient solving of hard 3-SAT formulae , 2001, IJCAI.
[2] Rina Dechter,et al. Constraint Processing , 1995, Lecture Notes in Computer Science.
[3] Roberto J. Bayardo,et al. Using CSP Look-Back Techniques to Solve Exceptionally Hard SAT Instances , 1996, CP.
[4] Claudio Sartori,et al. Incremental maintenance of multi-source views , 2001, ADC.
[5] Gregory Piatetsky-Shapiro,et al. Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.
[6] Niklas Sörensson,et al. An Extensible SAT-solver , 2003, SAT.
[7] R. L. Stens,et al. Sampling theory in Fourier and signal analysis : advanced topics , 1999 .
[8] James C. French,et al. Clustering large datasets in arbitrary metric spaces , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).
[9] Abdul Sattar,et al. Advances in Local Search for Satisfiability , 2007, Australian Conference on Artificial Intelligence.
[10] Bart Selman,et al. Ten Challenges in Propositional Reasoning and Search , 1997, IJCAI.
[11] Daniel A. Keim,et al. An Efficient Approach to Clustering in Large Multimedia Databases with Noise , 1998, KDD.
[12] Philip K. Chan,et al. Advances in Distributed and Parallel Knowledge Discovery , 2000 .
[13] Hans van Maaren,et al. Whose side are you on? Finding solutions in a biased search-tree , 2008, J. Satisf. Boolean Model. Comput..
[14] Maria Luisa Bonet,et al. Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) , 2022 .
[15] Hans-Peter Kriegel,et al. Incremental Clustering for Mining in a Data Warehousing Environment , 1998, VLDB.
[16] Tian Zhang,et al. BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.
[17] Hans-Peter Kriegel,et al. OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.
[18] Philip S. Yu,et al. Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..
[19] Andreas Rudolph,et al. Techniques of Cluster Algorithms in Data Mining , 2002, Data Mining and Knowledge Discovery.
[20] Bart Selman,et al. Ten Challenges Redux: Recent Progress in Propositional Reasoning and Search , 2003, CP.
[21] Bart Selman,et al. Regular Random k-SAT: Properties of Balanced Formulas , 2005, Journal of Automated Reasoning.
[22] P. J. Green,et al. Density Estimation for Statistics and Data Analysis , 1987 .
[23] Hillol Kargupta,et al. Distributed Clustering Using Collective Principal Component Analysis , 2001, Knowledge and Information Systems.
[24] Bart Selman,et al. Backdoors To Typical Case Complexity , 2003, IJCAI.
[25] Hillol Kargupta,et al. Collective, Hierarchical Clustering from Distributed, Heterogeneous Data , 1999, Large-Scale Parallel Data Mining.
[26] E. Parzen. On Estimation of a Probability Density Function and Mode , 1962 .
[27] Chu Min Li,et al. Look-Ahead Versus Look-Back for Satisfiability Problems , 1997, CP.
[28] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[29] Erich Schikuta,et al. Grid-clustering: an efficient hierarchical clustering method for very large data sets , 1996, Proceedings of 13th International Conference on Pattern Recognition.