Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A platform based on the multi-dimensional data modal for analysis of bio-molecular structures
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Substructure clustering: a novel mining paradigm for arbitrary data types
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
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In this paper, we will look at substructure clustering ofsequentail 3d objects.A sequential 3d object is a set ofpoints located in a three dimensional space that are linkedup to form a sequence.Given a set of sequential 3d objects,our aim is to find significantly large substructures whichare present in many of the sequential 3d objects.Unliketraditional subspace clustering methods in which objectsare compared based on values in the same dimension, thematching dimensions between two 3d sequential objects areaffected by both the translation and rotation of the objectsand are thus not well defined.Instead, similarity betweenthe objects are judge by computing a structural distancemeasurement call rmsd(Root Mean Square Distance)which require proper alignment (including translation androtation) of the objects.As the computation of rmsd isexpensive, we proposed a new measure call ald(AngelLength Distance) which is shown experimentally to approximatermsd.Based on ald, we define a new clusteringmodel called sCluster and devise an algorithm for discoveringall maximum sCluster in a 3d sequentail dataset.Experiments are conducted to illustrate the efficiency andeffectiveness of our algorithm.