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PAKDD
2005
ACM
120views Data Mining» more  PAKDD 2005»
15 years 10 months ago
Speeding-Up Hierarchical Agglomerative Clustering in Presence of Expensive Metrics
In several contexts and domains, hierarchical agglomerative clustering (HAC) offers best-quality results, but at the price of a high complexity which reduces the size of datasets ...
Mirco Nanni
GECCO
2004
Springer
124views Optimization» more  GECCO 2004»
15 years 10 months ago
Clustering with Niching Genetic K-means Algorithm
GA-based clustering algorithms often employ either simple GA, steady state GA or their variants and fail to consistently and efficiently identify high quality solutions (best known...
Weiguo Sheng, Allan Tucker, Xiaohui Liu
NAACL
2004
15 years 6 months ago
Name Tagging with Word Clusters and Discriminative Training
We present a technique for augmenting annotated training data with hierarchical word clusters that are automatically derived from a large unannotated corpus. Cluster membership is...
Scott Miller, Jethran Guinness, Alex Zamanian
NIPS
2001
15 years 6 months ago
Spectral Relaxation for K-means Clustering
The popular K-means clustering partitions a data set by minimizing a sum-of-squares cost function. A coordinate descend method is then used to nd local minima. In this paper we sh...
Hongyuan Zha, Xiaofeng He, Chris H. Q. Ding, Ming ...
ICCD
2003
IEEE
145views Hardware» more  ICCD 2003»
16 years 1 months ago
Care Bit Density and Test Cube Clusters: Multi-Level Compression Opportunities
: Most of the recently discussed and commercially introduced test stimulus data compression techniques are based on low care bit densities found in typical scan test vectors. Data ...
Bernd Könemann