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KDD
2005
ACM
166views Data Mining» more  KDD 2005»
16 years 7 months ago
A general model for clustering binary data
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This p...
Tao Li
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
16 years 7 months ago
Kernel k-means: spectral clustering and normalized cuts
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
KDD
2003
ACM
122views Data Mining» more  KDD 2003»
16 years 7 months ago
Natural communities in large linked networks
We are interested in finding natural communities in largescale linked networks. Our ultimate goal is to track changes over time in such communities. For such temporal tracking, we...
John E. Hopcroft, Omar Khan, Brian Kulis, Bart Sel...
KDD
2002
ACM
197views Data Mining» more  KDD 2002»
16 years 7 months ago
SimRank: a measure of structural-context similarity
The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents...
Glen Jeh, Jennifer Widom
KDD
2001
ACM
166views Data Mining» more  KDD 2001»
16 years 7 months ago
Generalized clustering, supervised learning, and data assignment
Clustering algorithms have become increasingly important in handling and analyzing data. Considerable work has been done in devising effective but increasingly specific clustering...
Annaka Kalton, Pat Langley, Kiri Wagstaff, Jungsoo...