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KDD
2008
ACM
140views Data Mining» more  KDD 2008»
16 years 6 months ago
On updates that constrain the features' connections during learning
In many multiclass learning scenarios, the number of classes is relatively large (thousands,...), or the space and time efficiency of the learning system can be crucial. We invest...
Omid Madani, Jian Huang 0002
KDD
2006
ACM
198views Data Mining» more  KDD 2006»
16 years 6 months ago
Estimating the global pagerank of web communities
Localized search engines are small-scale systems that index a particular community on the web. They offer several benefits over their large-scale counterparts in that they are rel...
Jason V. Davis, Inderjit S. Dhillon
KDD
2006
ACM
112views Data Mining» more  KDD 2006»
16 years 6 months ago
K-means clustering versus validation measures: a data distribution perspective
K-means is a widely used partitional clustering method. While there are considerable research efforts to characterize the key features of K-means clustering, further investigation...
Hui Xiong, Junjie Wu, Jian Chen
KDD
2005
ACM
166views Data Mining» more  KDD 2005»
16 years 6 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 6 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