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ISAAC
2009
Springer
175views Algorithms» more  ISAAC 2009»
14 years 3 months ago
Worst-Case and Smoothed Analysis of k-Means Clustering with Bregman Divergences
The k-means algorithm is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice. Most of the theoretical work is restricted to the c...
Bodo Manthey, Heiko Röglin
SIGIR
2009
ACM
14 years 3 months ago
Building enriched document representations using aggregated anchor text
It is well known that anchor text plays a critical role in a variety of search tasks performed over hypertextual domains, including enterprise search, wiki search, and web search....
Donald Metzler, Jasmine Novak, Hang Cui, Srihari R...
WEBI
2007
Springer
14 years 3 months ago
K-SVMeans: A Hybrid Clustering Algorithm for Multi-Type Interrelated Datasets
Identification of distinct clusters of documents in text collections has traditionally been addressed by making the assumption that the data instances can only be represented by ...
Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee G...
VISUALIZATION
2003
IEEE
14 years 2 months ago
Accelerating Large Data Analysis by Exploiting Regularities
We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (al...
David Ellsworth, Patrick J. Moran
NSPW
2003
ACM
14 years 2 months ago
Locality: a new paradigm for thinking about normal behavior and outsider threat
Locality as a unifying concept for understanding the normal behavior of benign users of computer systems is suggested as a unifying paradigm that will support the detection of mal...
John McHugh, Carrie Gates