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» Clustering performance data efficiently at massive scales
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101
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VLDB
1998
ACM
112views Database» more  VLDB 1998»
15 years 5 months ago
Incremental Clustering for Mining in a Data Warehousing Environment
Data warehouses provide a great deal of opportunities for performing data mining tasks such as classification and clustering. Typically, updates are collected and applied to the d...
Martin Ester, Hans-Peter Kriegel, Jörg Sander...
100
Voted
JGTOOLS
2008
100views more  JGTOOLS 2008»
15 years 24 days ago
Proximity Cluster Trees
Hierarchical spatial data structures provide a means for organizing data for efficient processing. Most spatial data structures are optimized for performing queries, such as inters...
Elena Jakubiak Hutchinson, Sarah F. Frisken, Ronal...
125
Voted
TC
2010
14 years 11 months ago
Network-on-Chip Hardware Accelerators for Biological Sequence Alignment
—The most pervasive compute operation carried out in almost all bioinformatics applications is pairwise sequence homology detection (or sequence alignment). Due to exponentially ...
Souradip Sarkar, Gaurav Ramesh Kulkarni, Partha Pr...
126
Voted
CORR
2008
Springer
132views Education» more  CORR 2008»
15 years 27 days ago
Materialized View Selection by Query Clustering in XML Data Warehouses
XML data warehouses form an interesting basis for decision-support applications that exploit complex data. However, native XML database management systems currently bear limited p...
Hadj Mahboubi, Kamel Aouiche, Jérôme ...
107
Voted
ICCV
2009
IEEE
16 years 5 months ago
Mode-Detection via Median-Shift
Median-shift is a mode seeking algorithm that relies on computing the median of local neighborhoods, instead of the mean. We further combine median-shift with Locality Sensitive...
Lior Shapira, Shai Avidan, Ariel Shamir