Robust regression and outlier detection
Robust regression and outlier detection
Randomized algorithms
Data mining and knowledge discovery in databases
Communications of the ACM
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Combinatorial Algorithms: Theory and Practice
Combinatorial Algorithms: Theory and Practice
Fast and robust general purpose clustering algorithms
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Hybrid genetic algorithms are better for spatial clustering
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Categorizing Visitors Dynamically by Fast and Robust Clustering of Access Logs
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
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A fundamental procedure appearing within such clustering methods as k-Means, Expectation Maximization, Fuzzy-C-Means and Minimum Message Length is that of computing estimators of location. Most estimators of location exhibiting useful robustness properties require at least quadratic time to compute, far too slow for large data mining applications. In this paper, we propose O(Dn√n)-time randomized algorithms for computing robust estimators of location, where n is the size of the data set, and D is the dimension.