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» Hierarchical exploration of large multivariate data sets
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ICPP
2000
IEEE
15 years 2 months ago
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
Clustering is a data mining problem which finds dense regions in a sparse multi-dimensional data set. The attribute values and ranges of these regions characterize the clusters. ...
Harsha S. Nagesh, Sanjay Goil, Alok N. Choudhary
VISUALIZATION
1995
IEEE
15 years 1 months ago
Recursive Pattern: A Technique for Visualizing Very Large Amounts of Data
Animportantgoalofvisualizationtechnologyistosupport the exploration and analysis of very large amounts of data. In this paper, we propose a new visualization technique called ‘r...
Daniel A. Keim, Mihael Ankerst, Hans-Peter Kriegel
SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
15 years 9 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
ICCAD
2006
IEEE
108views Hardware» more  ICCAD 2006»
15 years 6 months ago
Formal model of data reuse analysis for hierarchical memory organizations
– In real-time data-dominated communication and multimedia processing applications, due to the manipulation of large sets of data, a multi-layer memory hierarchy is used to enhan...
Ilie I. Luican, Hongwei Zhu, Florin Balasa
VISUALIZATION
2005
IEEE
15 years 3 months ago
Interactive Rendering of Large Unstructured Grids Using Dynamic Level-of-Detail
We describe a new dynamic level-of-detail (LOD) technique that allows real-time rendering of large tetrahedral meshes. Unlike approaches that require hierarchies of tetrahedra, ou...
Steven P. Callahan, João Luiz Dihl Comba, P...