Algorithms for clustering data
Algorithms for clustering data
Topology of strings: median string is NP-complete
Theoretical Computer Science
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
An approximate median search algorithm in non-metric spaces
Pattern Recognition Letters
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
"More Like This" or "Not for Me": Delivering Personalised Recommendations in Multi-user Environments
UM '07 Proceedings of the 11th international conference on User Modeling
Hi-index | 0.00 |
The k-means algorithm is a well-known clustering method. Although this technique was initially defined for a vector representation of the data, the set median (the point belonging to a set P that minimizes the sum of distances to the rest of points in P) can be used instead of the mean when this vectorial representation is not possible. The computational cost of the set median is O(|P|2). Recently, a new method to obtain an approximated median in O(|P|) was proposed. In this paper we use this approximated median in the k-median algorithm to speed it up.