BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
SS-ClusterTree: a subspace clustering based indexing algorithm over high-dimensional image features
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Hi-index | 0.00 |
Nowadays large volumes of data with high dimensionality are being generated in many fields. ClusterTree is a new indexing approach representing clusters generated by any existing clustering approach. It supports effective and efficient image retrieval. Lots of clustering algorithms have been developed, and in most of them some parameters should be determined by hand. The authors propose a new ClusterTree structure, which based on the improved CLIQUE and avoids any parameters defined by user. Using multiresolution property of wavelet transforms, the proposed approach can cluster at different resolution and remain the relation between these clusters to construct hierarchical index. The results of the application confirm that the ClusterTree is very applicable and efficient.