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ICML
2010
IEEE
15 years 4 months ago
Comparing Clusterings in Space
This paper proposes a new method for comparing clusterings both partitionally and geometrically. Our approach is motivated by the following observation: the vast majority of previ...
Michael H. Coen, M. Hidayath Ansari, Nathanael Fil...
TNN
2010
155views Management» more  TNN 2010»
15 years 25 days ago
Incorporating the loss function into discriminative clustering of structured outputs
Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations ac...
Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. ...
ICPR
2002
IEEE
16 years 7 months ago
Fast Hierarchical Clustering Based on Compressed Data
: One way to scale up clustering algorithms is to squash the data by some intelligent compression technique and cluster only the compressed data records. Such compressed data recor...
Erendira Rendon, Ricardo Barandela
COCOON
2010
Springer
15 years 11 months ago
Clustering with or without the Approximation
We study algorithms for clustering data that were recently proposed by Balcan, Blum and Gupta in SODA’09 [4] and that have already given rise to two follow-up papers. The input f...
Frans Schalekamp, Michael Yu, Anke van Zuylen
ICDM
2005
IEEE
146views Data Mining» more  ICDM 2005»
15 years 11 months ago
Merging Interface Schemas on the Deep Web via Clustering Aggregation
We consider the problem of integrating a large number of interface schemas over the Deep Web, The scale of the problem and the diversity of the sources present serious challenges ...
Wensheng Wu, AnHai Doan, Clement T. Yu