Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Three-dimensional alpha shapes
VVS '92 Proceedings of the 1992 workshop on Volume visualization
Three-dimensional alpha shapes
ACM Transactions on Graphics (TOG)
Multidimensional access methods
ACM Computing Surveys (CSUR)
A new Voronoi-based surface reconstruction algorithm
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Making B+- trees cache conscious in main memory
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A classifier for semi-structured documents
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Genome scale prediction of protein functional class from sequence using data mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Delaunay based shape reconstruction from large data
PVG '01 Proceedings of the IEEE 2001 symposium on parallel and large-data visualization and graphics
Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications
Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications
Computing Smooth Molecular Surfaces
IEEE Computer Graphics and Applications
Discovering Structural Association of Semistructured Data
IEEE Transactions on Knowledge and Data Engineering
Finding Patterns in Three-Dimensional Graphs: Algorithms and Applications to Scientific Data Mining
IEEE Transactions on Knowledge and Data Engineering
Information Sciences—Applications: An International Journal
alpha-Surface and Its Application to Mining Protein Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A 3D Molecular Surface Representation Supporting Neighborhood Queries
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
MotifMiner: A General Toolkit for Efficiently Identifying Common Substructures in Molecules
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Discovering Frequent Geometric Subgraphs
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Discovery of Common Substructures in Macromolecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
\Delta B + Tree: Indexing 3D Point Sets for Pattern Discovery
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Parallel algorithms for mining frequent structural motifs in scientific data
Proceedings of the 18th annual international conference on Supercomputing
Clustering techniques for protein surfaces
Pattern Recognition
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A successful application of data mining to bioinformatics is protein classification. A number of techniques have been developed to classify proteins according to important features in their sequences, secondary structures, or three-dimensional structures. In this paper, we introduce a novel approach to protein classification based on significant patterns discovered on the surface of a protein. We define a notion called \alpha{\hbox{-}}{\rm{surface}}. We discuss the geometric properties of \alpha{\hbox{-}}{\rm{surface}} and present an algorithm that calculates the \alpha{\hbox{-}}{\rm{surface}} from a finite set of points in R^{3}. We apply the algorithm to extracting the \alpha{\hbox{-}}{\rm{surface}} of a protein and use a pattern discovery algorithm to discover frequently occurring patterns on the surfaces. The pattern discovery algorithm utilizes a new index structure called the \Delta{\rm{B}}^{+} tree. We use these patterns to classify the proteins. While most existing techniques focus on the binary classification problem, we apply our approach to classifying three families of proteins. Experimental results show the good performance of the proposed approach.