Algorithms for clustering data
Algorithms for clustering data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Subspace tracking with adaptive threshold rank estimation
Journal of VLSI Signal Processing Systems - Special issue on array optimization and adaptive tracking algorithms
An eigenspace update algorithm for image analysis
Graphical Models and Image Processing
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Fast Nearest-Neighbor Algorithm Based on a Principal Axis Search Tree
IEEE Transactions on Pattern Analysis and Machine Intelligence
Techniques of Cluster Algorithms in Data Mining
Data Mining and Knowledge Discovery
Clustering for Approximate Similarity Search in High-Dimensional Spaces
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
IEEE Transactions on Signal Processing
Iteration-free clustering algorithm for nonstationary image database
IEEE Transactions on Multimedia
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Content-based information retrieval (CBIR) of multimedia data is an active research topic in intelligent information retrieval field. To support CBIR, high-dimensional data indexing and query is a challenging problem due to the inherent high dimension of multimedia data. Clustering-based indexing structures have been proved to be efficient for high-dimensional data indexing. However, most clustering-based indexing structures are static, in which new data cannot be inserted by just modifying the existing clusters or indexing structures. To resolve this problem, a two-level indexing method, called IASDS plus IPAT method, is developed in this paper. At the IASDS level, clusters and the corresponding subspaces can be incrementally updated, while the indexing structures within the clusters can be incrementally updated at the IPAT level. Furthermore, the proposed IASDS plus IPAT method is able to balance indexing efficiency and query accuracy by choosing an appropriate number of children nodes. The experimental results show that the IASDS plus IPAT method is very efficient for updating clusters and indexing structures with newly inserted data, and that its query accuracy is only slightly degraded while its query time is almost the same in comparison with the similar indexing structure built by non-incremental method.