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» Discriminative cluster analysis
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119
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SODA
2010
ACM
189views Algorithms» more  SODA 2010»
16 years 28 days ago
Correlation Clustering with Noisy Input
Correlation clustering is a type of clustering that uses a basic form of input data: For every pair of data items, the input specifies whether they are similar (belonging to the s...
Claire Mathieu, Warren Schudy
161
Voted
BIOINFORMATICS
2007
190views more  BIOINFORMATICS 2007»
15 years 3 months ago
Towards clustering of incomplete microarray data without the use of imputation
Motivation: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by...
Dae-Won Kim, Ki Young Lee, Kwang H. Lee, Doheon Le...
113
Voted
KDD
2004
ACM
103views Data Mining» more  KDD 2004»
16 years 4 months ago
An objective evaluation criterion for clustering
We propose and test an objective criterion for evaluation of clustering performance: How well does a clustering algorithm run on unlabeled data aid a classification algorithm? The...
Arindam Banerjee, John Langford
EMNLP
2007
15 years 5 months ago
Learning to Merge Word Senses
It has been widely observed that different NLP applications require different sense granularities in order to best exploit word sense distinctions, and that for many applications ...
Rion Snow, Sushant Prakash, Daniel Jurafsky, Andre...
111
Voted
ICTIR
2009
Springer
15 years 10 months ago
A New Measure of the Cluster Hypothesis
Abstract. We have found that the nearest neighbor (NN) test is an insufficient measure of the cluster hypothesis. The NN test is a local measure of the cluster hypothesis. Designer...
Mark D. Smucker, James Allan