Algorithms for clustering data
Algorithms for clustering data
A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Content-Based Indexing of Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Proximity Matching Using Fixed-Queries Trees
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Clustering Large Datasets in Arbitrary Metric Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
IEEE Transactions on Knowledge and Data Engineering
A unified framework for model-based clustering
The Journal of Machine Learning Research
A Data Structure and an Algorithm for the Nearest Point Problem
IEEE Transactions on Software Engineering
Foveated shot detection for video segmentation
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.00 |
In this paper we introduce a new indexing approach to representing multimedia object classes generated by the Expectation Maximization clustering algorithm in a balanced and dynamic tree structure. To this aim the EM algorithm has been modified in order to obtain at each step of its recursive application balanced clusters. In this manner our tree provides a simple and practical solution to index clustered data and support efficient retrieval of the nearest neighbors in high dimensional object spaces.