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» Mining evolving data streams for frequent patterns
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PKDD
2009
Springer
134views Data Mining» more  PKDD 2009»
15 years 4 months ago
Mining Graph Evolution Rules
In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern that describe the evolution of large networks over time, at a local level. Given a sequenc...
Michele Berlingerio, Francesco Bonchi, Björn ...
ADMA
2008
Springer
87views Data Mining» more  ADMA 2008»
14 years 11 months ago
Negative Generator Border for Effective Pattern Maintenance
In this paper, we study the maintenance of frequent patterns in the context of the generator representation. The generator representation is a concise and lossless representation o...
Mengling Feng, Jinyan Li, Limsoon Wong, Yap-Peng T...
CIDM
2007
IEEE
15 years 4 months ago
Structure Prediction in Temporal Networks using Frequent Subgraphs
— There are several types of processes which can be modeled explicitly by recording the interactions between a set of actors over time. In such applications, a common objective i...
Mayank Lahiri, Tanya Y. Berger-Wolf
PAKDD
2009
ACM
87views Data Mining» more  PAKDD 2009»
15 years 4 months ago
Application-Independent Feature Construction from Noisy Samples
When training classifiers, presence of noise can severely harm their performance. In this paper, we focus on “non-class” attribute noise and we consider how a frequent fault-t...
Dominique Gay, Nazha Selmaoui, Jean-Françoi...
ICDM
2008
IEEE
117views Data Mining» more  ICDM 2008»
15 years 4 months ago
RTM: Laws and a Recursive Generator for Weighted Time-Evolving Graphs
How do real, weighted graphs change over time? What patterns, if any, do they obey? Earlier studies focus on unweighted graphs, and, with few exceptions, they focus on static snap...
Leman Akoglu, Mary McGlohon, Christos Faloutsos