Amortized efficiency of list update and paging rules
Communications of the ACM
Data structures and network algorithms
Data structures and network algorithms
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Multiservice Allocation in Hierarchical Cellular Networks (MAHCN)
ISCC '99 Proceedings of the The Fourth IEEE Symposium on Computers and Communications
Optimal implementations of UPGMA and other common clustering algorithms
Information Processing Letters
Discrete Applied Mathematics
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
ACM Transactions on Knowledge Discovery from Data (TKDD)
A GA-Based Feature Selection for High-Dimensional Data Clustering
WGEC '09 Proceedings of the 2009 Third International Conference on Genetic and Evolutionary Computing
Clustering based on kolmogorov information
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
On improving the performance of IEEE 802.11s based wireless mesh networks using directional antenna
LCN '10 Proceedings of the 2010 IEEE 35th Conference on Local Computer Networks
Optimal clustering method in ultrametric spaces
ISCC '11 Proceedings of the 2011 IEEE Symposium on Computers and Communications
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We introduce in this paper a competitive unsupervised clustering algorithm which has two strong features: it is fast and flexible on the processed data type as well as in terms of precision. Our approach has a computational cost, in the worst case, of O(n^2)+ ε, and in the average case, of O(n)+ ε. This complexity is due to the use of ultrametric distance properties. We create an ultrametric space from a sample data, chosen uniformly at random, in order to obtain a global view of proximities in the data set according to the similarity criterion. Then, we use this proximity profile to cluster the global set. We present two examples of our algorithm and compare our results with those of a classic clustering method.