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ICML
2004
IEEE
14 years 9 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
TCAD
2002
99views more  TCAD 2002»
13 years 8 months ago
Analysis of on-chip inductance effects for distributed RLC interconnects
This paper introduces an accurate analysis of on-chip inductance effects for distributed interconnects that takes the effect of both the series resistance and the output parasitic ...
Kaustav Banerjee, Amit Mehrotra
KDD
2009
ACM
248views Data Mining» more  KDD 2009»
14 years 1 months ago
PSkip: estimating relevance ranking quality from web search clickthrough data
1 In this article, we report our efforts in mining the information encoded as clickthrough data in the server logs to evaluate and monitor the relevance ranking quality of a commer...
Kuansan Wang, Toby Walker, Zijian Zheng
PVLDB
2008
182views more  PVLDB 2008»
13 years 8 months ago
SCOPE: easy and efficient parallel processing of massive data sets
Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, pr...
Ronnie Chaiken, Bob Jenkins, Per-Åke Larson,...
SC
2005
ACM
14 years 2 months ago
Parallel Programmer Productivity: A Case Study of Novice Parallel Programmers
In developing High-Performance Computing (HPC) software, time to solution is an important metric. This metric is comprised of two main components: the human effort required develo...
Lorin Hochstein, Jeffrey Carver, Forrest Shull, Si...