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KDD
2008
ACM
172views Data Mining» more  KDD 2008»
16 years 6 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
KAIS
2000
87views more  KAIS 2000»
15 years 6 months ago
An Index Structure for Data Mining and Clustering
Abstract. In this paper we present an index structure, called MetricMap, that takes a set of objects and a distance metric and then maps those objects to a k-dimensional space in s...
Xiong Wang, Jason Tsong-Li Wang, King-Ip Lin, Denn...
BCB
2010
150views Bioinformatics» more  BCB 2010»
15 years 1 months ago
Enzymatic target identification with dynamic states
As a metabolic network reaches from a state to a steady state, a subset of its fluxes gradually change. The sequence of intermediate states, called the dynamic state, shows the pa...
Bin Song, Sanjay Ranka, Tamer Kahveci
IWPC
2009
IEEE
16 years 1 months ago
Vector space analysis of software clones
In this paper, we introduce a technique for applying Independent Component Analysis to vector space representations of software code fragments such as methods or blocks. The dista...
Scott Grant, James R. Cordy
WIRN
2005
Springer
15 years 12 months ago
Ensembles Based on Random Projections to Improve the Accuracy of Clustering Algorithms
We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtai...
Alberto Bertoni, Giorgio Valentini