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KDD
1998
ACM
123views Data Mining» more  KDD 1998»
15 years 10 months ago
Scaling Clustering Algorithms to Large Databases
Practical clustering algorithms require multiple data scans to achieve convergence. For large databases, these scans become prohibitively expensive. We present a scalable clusteri...
Paul S. Bradley, Usama M. Fayyad, Cory Reina
ICS
2004
Tsinghua U.
15 years 11 months ago
Cluster prefetch: tolerating on-chip wire delays in clustered microarchitectures
The growing dominance of wire delays at future technology points renders a microprocessor communication-bound. Clustered microarchitectures allow most dependence chains to execute...
Rajeev Balasubramonian
ICDM
2002
IEEE
122views Data Mining» more  ICDM 2002»
15 years 11 months ago
Using Category-Based Adherence to Cluster Market-Basket Data
In this paper, we devise an efficient algorithm for clustering market-basket data. Different from those of the traditional data, the features of market-basket data are known to b...
Ching-Huang Yun, Kun-Ta Chuang, Ming-Syan Chen
BMCBI
2006
134views more  BMCBI 2006»
15 years 6 months ago
An approach for clustering gene expression data with error information
Background: Clustering of gene expression patterns is a well-studied technique for elucidating trends across large numbers of transcripts and for identifying likely co-regulated g...
Brian Tjaden
PKDD
2000
Springer
144views Data Mining» more  PKDD 2000»
15 years 9 months ago
Fast Hierarchical Clustering Based on Compressed Data and OPTICS
: One way to scale up clustering algorithms is to squash the data by some intelligent compression technique and cluster only the compressed data records. Such compressed data recor...
Markus M. Breunig, Hans-Peter Kriegel, Jörg S...