论文信息 - Inductive Databases of Polynomial Equations

Inductive Databases of Polynomial Equations

Inductive databases (IDBs) contain both data and patterns. Here we consider IDBs where patterns are polynomial equations. We present a constraint-based approach to answering inductive queries in this domain. The approach is based on heuristic search through the space of polynomial equations and can use subsumption and evaluation constraints on polynomial equations. We evaluate this approach on standard regression problems. We finally consider IDBs containing patterns in the form of polynomial equations as well as molecular fragments, where the two are combined in order to derive QSAR (Quantitative Structure-Activity Relationships) models.

[1] Rajeev Rastogi,et al. Scalable data mining with model constraints , 2000, SKDD.

[2] Heikki Mannila,et al. Levelwise Search and Borders of Theories in Knowledge Discovery , 1997, Data Mining and Knowledge Discovery.

[3] Saso Dzeroski,et al. Experiments in Predicting Biodegradability , 1999, ILP.

[4] Sago Deroski,et al. Discovering Dynamics: From Inductive Logic Programming To Machine Discovery , 2002 .

[5] Luc De Raedt,et al. Feature Construction with Version Spaces for Biochemical Applications , 2001, ICML.

[6] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[7] Bhavani M. Thuraisingham,et al. Data mining, national security, privacy and civil liberties , 2002, SKDD.

[8] Heikki Mannila,et al. A database perspective on knowledge discovery , 1996, CACM.

[9] Luc De Raedt,et al. Data Mining as Constraint Logic Programming , 2002, Computational Logic: Logic Programming and Beyond.

[10] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .