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» Graph Mining using Graph Pattern Profiles
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135
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PR
2007
141views more  PR 2007»
15 years 2 months ago
A Riemannian approach to graph embedding
In this paper, we make use of the relationship between the Laplace–Beltrami operator and the graph Laplacian, for the purposes of embedding a graph onto a Riemannian manifold. T...
Antonio Robles-Kelly, Edwin R. Hancock
132
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KDD
2002
ACM
147views Data Mining» more  KDD 2002»
16 years 3 months ago
Visualized Classification of Multiple Sample Types
The goal of the knowledge discovery and data mining is to extract the useful knowledge from the given data. Visualization enables us to find structures, features, patterns, and re...
Li Zhang, Aidong Zhang, Murali Ramanathan
127
Voted
ICMLA
2004
15 years 4 months ago
Outlier detection and evaluation by network flow
Detecting outliers is an important topic in data mining. Sometimes the outliers are more interesting than the rest of the data. Outlier identification has lots of applications, su...
Ying Liu, Alan P. Sprague
123
Voted
KDID
2003
119views Database» more  KDID 2003»
15 years 4 months ago
Generalized Version Space Trees
We introduce generalized version space trees, a novel data structure that serves as a condensed representation in inductive databases for graph mining. Generalized version space tr...
Ulrich Rückert, Stefan Kramer
96
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ICSE
2003
IEEE-ACM
16 years 2 months ago
Whole Program Path-Based Dynamic Impact Analysis
Impact analysis, determining when a change in one part of a program affects other parts of the program, is timeconsuming and problematic. Impact analysis is rarely used to predict...
James Law, Gregg Rothermel