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89
Voted
IBPRIA
2003
Springer
15 years 4 months ago
2003
Springer
A straightforward and efficient way to discover clustering tendencies in data using a recently proposed Maximum Variance Clustering algorithm is proposed. The approach shares the ...
89
Voted
BMVC
1998
15 years 1 months ago
1998
This paper describes a method for the segmentation of dynamic data. It extends well known algorithms developed in the context of static clustering (e.g., the c-means algorithm, Ko...
135
Voted
ICAI
2004
15 years 1 months ago
2004
This paper centers on the discussion of k-medoid-style clustering algorithms for supervised summary generation. This task requires clustering techniques that identify class-unifor...
97
Voted
CEC
2008
IEEE
15 years 6 months ago
2008
IEEE
—The conventional K-Means clustering algorithm must know the number of clusters in advance and the clustering result is sensitive to the selection of the initial cluster centroid...
106
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SIGMOD
1998
ACM
15 years 3 months ago
1998
ACM
Data mining applications place special requirements on clustering algorithms including: the ability to nd clusters embedded in subspaces of high dimensional data, scalability, end...