Finding Patterns on Protein Surfaces: Algorithms and Applications to Protein Classification
IEEE Transactions on Knowledge and Data Engineering
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Three-dimensional point sets can be used to representdata in different domains. Given a database of 3D pointsets, pattern discovery looks for similar subsets that occurin multiple point sets. Geometric hashing proved to be aneffective technique in discovering patterns in 3D point sets.However, there are also known shortcomings. We proposea new indexing technique called \Delta B+Trees. It is an extensionof B+-Trees that stores point triplet information. Itovercomes the shortcomings of the geometric hashing technique.We introduce four different ways of constructing thekey from a triplet. We give analytical comparison betweenthe new index structure and the geometric hashing technique.We also conduct experiments on both synthetic dataand real data to evaluate the performance.