Substructure Clustering on Sequential 3d Object Datasets

In this paper, we will look at substructure clustering of sequential 3d objects. A sequential 3d object is a set of points located in a three dimensional space that are linked up to form a sequence. Given a set of sequential 3d objects, our aim is to find significantly large substructures which are present in many of the sequential 3d objects. Unlike traditional subspace clustering methods in which objects are compared based on values in the same dimension, the matching dimensions between two 3d sequential objects are affected by both the translation and rotation of the objects and are thus not well defined. Instead, similarity between the objects are judge by computing a structural distance measurement call ?? ??? (?????? "! $#&% ? ? '(") which require proper alignment (including translation and rotation) of the objects. As the computation of ?? ??? is expensive, we proposed a new measure call 0 ?2143 65 ?0 7 65 ?98@#&% ? ? '(") which is sho...

• Type: Conference
• Year: 2004
• Where: ICDE
• Authors: Zhenqiang Tan, Anthony K. H. Tung
• Keywords: Database, ICDE 2004, Sequential 3d Objects, Sequential Dataset, Sequential Objects