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SDM
2008
SIAM
168views Data Mining» more  SDM 2008»
15 years 3 months ago
Semi-Supervised Clustering via Matrix Factorization
The recent years have witnessed a surge of interests of semi-supervised clustering methods, which aim to cluster the data set under the guidance of some supervisory information. U...
Fei Wang, Tao Li, Changshui Zhang
PDPTA
2000
15 years 3 months ago
Performance Monitoring on an HPVM Cluster
Clusters of workstations are becoming popular platforms for parallel computing, but performance on these systems is more complex and harder to predict than on traditional parallel...
Geetanjali Sampemane, Scott Pakin, Andrew A. Chien
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
16 years 2 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
ADBIS
2007
Springer
132views Database» more  ADBIS 2007»
15 years 8 months ago
Clustering Approach to Generalized Pattern Identification Based on Multi-instanced Objects with DARA
Clustering is an essential data mining task with various types of applications. Traditional clustering algorithms are based on a vector space model representation. A relational dat...
Rayner Alfred, Dimitar Kazakov
SAC
2008
ACM
15 years 1 months ago
Efficient concept clustering for ontology learning using an event life cycle on the web
Ontology learning integrates many complementary techniques, including machine learning, natural language processing, and data mining. Specifically, clustering techniques facilitat...
Sangsoo Sung, Seokkyung Chung, Dennis McLeod