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» Measuring the Quality of Approximated Clusterings
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ICMLA
2008
15 years 1 months ago
Farthest Centroids Divisive Clustering
A method is presented to partition a given set of data entries embedded in Euclidean space by recursively bisecting clusters into smaller ones. The initial set is subdivided into ...
Haw-ren Fang, Yousef Saad
CIDM
2009
IEEE
15 years 4 months ago
An architecture and algorithms for multi-run clustering
—This paper addresses two main challenges for clustering which require extensive human effort: selecting appropriate parameters for an arbitrary clustering algorithm and identify...
Rachsuda Jiamthapthaksin, Christoph F. Eick, Vadee...
AAIM
2007
Springer
118views Algorithms» more  AAIM 2007»
15 years 3 months ago
Significance-Driven Graph Clustering
Abstract. Modularity, the recently defined quality measure for clusterings, has attained instant popularity in the fields of social and natural sciences. We revisit the rationale b...
Marco Gaertler, Robert Görke, Dorothea Wagner
DAWAK
2007
Springer
15 years 5 months ago
MOSAIC: A Proximity Graph Approach for Agglomerative Clustering
Representative-based clustering algorithms are quite popular due to their relative high speed and because of their sound theoretical foundation. On the other hand, the clusters the...
Jiyeon Choo, Rachsuda Jiamthapthaksin, Chun-Sheng ...
DEXA
2007
Springer
140views Database» more  DEXA 2007»
15 years 5 months ago
Clustering-Based K-Anonymisation Algorithms
Abstract. K-anonymisation is an approach to protecting private information contained within a dataset. Many k-anonymisation methods have been proposed recently and one class of suc...
Grigorios Loukides, Jianhua Shao