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» Modeling Relational Data as Graphs for Mining
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PKDD
2010
Springer
235views Data Mining» more  PKDD 2010»
15 years 2 months ago
Online Structural Graph Clustering Using Frequent Subgraph Mining
The goal of graph clustering is to partition objects in a graph database into different clusters based on various criteria such as vertex connectivity, neighborhood similarity or t...
Madeleine Seeland, Tobias Girschick, Fabian Buchwa...
TSE
2008
113views more  TSE 2008»
15 years 4 months ago
Discovering Neglected Conditions in Software by Mining Dependence Graphs
Neglected conditions are an important but difficult-to-find class of software defects. This paper presents a novel approach for revealing neglected conditions that integrates stati...
Ray-Yaung Chang, Andy Podgurski, Jiong Yang
IJKDB
2010
162views more  IJKDB 2010»
15 years 1 months ago
New Trends in Graph Mining: Structural and Node-Colored Network Motifs
Searching for repeated features characterizing biological data is fundamental in computational biology. When biological networks are under analysis, the presence of repeated modul...
Francesco Bruno, Luigi Palopoli, Simona E. Rombo
KDD
2004
ACM
170views Data Mining» more  KDD 2004»
16 years 4 months ago
Why collective inference improves relational classification
Procedures for collective inference make simultaneous statistical judgments about the same variables for a set of related data instances. For example, collective inference could b...
David Jensen, Jennifer Neville, Brian Gallagher
DMIN
2006
151views Data Mining» more  DMIN 2006»
15 years 5 months ago
Rough Set Theory: Approach for Similarity Measure in Cluster Analysis
- Clustering of data is an important data mining application. One of the problems with traditional partitioning clustering methods is that they partition the data into hard bound n...
Shuchita Upadhyaya, Alka Arora, Rajni Jain