Knowledge discovery from structural data

Discovering repetitive substructure in a structural database improves the ability to interpret and compress the data. This paper describes the Subdue system that uses domain-independent and domain-dependent heuristics to find interesting and repetitive structures in structural data. This substructure discovery technique can be used to discover fuzzy concepts, compress the data description, and formulate hierarchical substructure definitions. Examples from the domains of scene analysis, chemical compound analysis, computer-aided design, and program analysis demonstrate the benefits of the discovery technique.

[1]  P. Langley,et al.  Concept formation in structured domains , 1991 .

[2]  David B. Leake.  Artiicial Intelligence , 2001 .

[3]  Douglas H. Fisher,et al.  Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.

[4]  R. Bharat Rao,et al.  Learning Engineering Models with the Minimum Description Length Principle , 1992, AAAI.

[5]  Hans-Jörg Kreowski,et al.  Grammatical Inference Based on Hyperedge Replacement , 1990, Graph-Grammars and Their Application to Computer Science.

[6]  Hiroshi Motoda,et al.  Unifying Learning Methods by Colored Digraphs , 1993, ALT.

[7]  Robert J. Schalkoff,et al.  Pattern recognition - statistical, structural and neural approaches , 1991 .

[8]  King-Sun Fu,et al.  Syntactic Pattern Recognition And Applications , 1968 .

[9]  Horst Bunke,et al.  Inexact graph matching for structural pattern recognition , 1983, Pattern Recognit. Lett..

[10]  Jörg Rech,et al.  Knowledge Discovery in Databases , 2001, Künstliche Intell..

[11]  Laurent Miclet,et al.  Structural Methods in Pattern Recognition , 1986 .

[12]  Yvan G. Leclerc,et al.  Constructing simple stable descriptions for image partitioning , 1989, International Journal of Computer Vision.

[13]  J. Rissanen.  Stochastic Complexity in Statistical Inquiry Theory , 1989 .

[14]  Lawrence B. Holder,et al.  Fuzzy Substructure Discovery , 1992, ML.

[15]  Mark Derthick,et al.  A Minimal Encoding Approach to Feature Discovery , 1991, AAAI.

[16]  Patrick Henry Winston,et al.  Learning structural descriptions from examples , 1970 .

[17]  Ronald L. Rivest,et al.  Inferring Decision Trees Using the Minimum Description Length Principle , 1989, Inf. Comput..

[18]  Lawrence B. Holder,et al.  Substucture Discovery in the SUBDUE System , 1994, KDD Workshop.

[19]  Jakub Segen.  Graph Clustering and Model Learning by Data Compression , 1990, ML.

[20]  David L. Waltz,et al.  Understanding Line drawings of Scenes with Shadows , 1975 .

[21]  Robert J. Schalkoff,et al.  Pattern recognition : statistical, structural and neural approaches / Robert J. Schalkoff , 1992 .

[22]  Robert Levinson,et al.  A Self-Organizing Retrieval System for Graphs , 1984, AAAI.

[23]  Edwin P. D. Pednault,et al.  Some Experiments in Applying Inductive Inference Principles to Surface Reconstruction , 1989, IJCAI.

[24]  James Kelly,et al.  AutoClass: A Bayesian Classification System , 1993, ML.

[25]  Alex Pentland,et al.  Part Segmentation for Object Recognition , 1989, Neural Computation.