Finding density-based subspace clusters in graphs with feature vectors

Data Mining and Knowledge Discovery

Volumn 25, Issue 2, 2012, Pages 243-269

  Günnemann, Stephan ^a   Boden, Brigitte ^a   Seidl, Thomas ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

Dense subgraphs; Graph clustering; Networks

Indexed keywords

ARBITRARY SHAPE; ATTRIBUTE INFORMATION; ATTRIBUTE SIMILARITY; CLUSTERING APPROACH; CLUSTERING SOLUTIONS; DATA SOURCE; DENSITY-BASED; FEATURE VECTORS; FIXED POINT ITERATION; GRAPH CLUSTERING; GRAPH DENSITY; KNOWLEDGE EXTRACTION; MINING TECHNIQUES; NETWORK INFORMATION; SUBGRAPH MINING; SUBGRAPHS; SUBSPACE CLUSTERING; SUBSPACE CLUSTERS;

CLUSTERING ALGORITHMS; NETWORKS (CIRCUITS);

VECTORS;

EID: 84864555859     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-012-0272-z     Document Type: Conference Paper

References (31)

1. Aggarwal C, Wang H (2010) Managing and mining graph data. Springer, New York
2. Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. In: SIGMOD, pp 94-105. SIGMOD, Seattle
3. Assent I, Krieger R, Müller E, Seidl T (2008) EDSC: efficient density-based subspace clustering. In: CIKM, pp 1093-1102. CIKM, Glasgow
4. Beyer KS, Goldstein J, Ramakrishnan R, Shaft U (1999) When is "nearest neighbor"meaningful? In: ICDT, pp 217-235. ICDT, Mont Blanc
5. Dorogovtsev S, Goltsev A, Mendes J (2006) K-core organization of complex networks. Phys Rev Lett 96(4):40-601
6. Du N, Wu B, Pei X, Wang B, Xu L (2007) Community detection in large-scale social networks. In: Web-KDD/SNA-KDD, pp 16-25. SNA-KDD, San Jose
7. Ester M, Kriegel HP, S J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp 226-231. KDD, Portland
8. Ester M, Ge R, Gao BJ, Hu Z, Ben-Moshe B (2006) Joint cluster analysis of attribute data and relationship data: The connected k-center problem. In: SDM. SDM, Bethesda
9. Günnemann S, Müller E, Färber I, Seidl T (2009) Detection of orthogonal concepts in subspaces of high dimensional data. In: CIKM, pp 1317-1326. CIKM, Hong Kong
10. Günnemann S, Färber I, Boden B, Seidl T (2010) Subspace clustering meets dense subgraph mining: A synthesis of two paradigms. In: ICDM, pp 845-850. ICDM, Sydney
11. Günnemann S, Kremer H, Seidl T (2010) Subspace clustering for uncertain data. In: SDM, pp 385-396. SDM, Bethesda
12. Günnemann S, Boden B, Seidl T (2011) DB-CSC: A density-based approach for subspace clustering in graphs with feature vectors. In: ECML/PKDD (1), pp 565-580. ECML, Athens
13. Günnemann S, Färber I, Müller E, Assent I, Seidl T (2011) External evaluation measures for subspace clustering. In: CIKM, pp 1363-1372. CIKM, Glasgow
14. HanischD, Zien A, ZimmerR, Lengauer T (2002) Co-clustering of biological networks and gene expression data. Bioinformatics 18:145-154
15. Hinneburg A,Keim DA(1998) An efficient approach to clustering in large multimedia databases with noise. In: KDD, pp 58-65. KDD, New York
16. Janson S, LuczakM (2007) Asimple solution to the k-core problem. Rand StructAlgorithm 30(1-2):50-62
17. Kailing K, Kriegel HP, Kroeger P (2004) Density-connected subspace clustering for high-dimensional data. In: SDM, pp 246-257. SDM, Bethesda
18. Kriegel HP, Kröger P, Zimek A (2009) Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. Trans Knowl Discov Data 3(1):1-58
19. Kubica J, Moore AW, Schneider JG (2003) Tractable group detection on large link data sets. In: ICDM, pp 573-576. ICDM, Sydney
20. Long B,Wu X, Zhang ZM, Yu PS (2006) Unsupervised learning on k-partite graphs. In: KDD, pp 317-326. KDD, Portland
21. Long B, Zhang ZM,Yu PS (2007)Aprobabilistic framework for relational clustering. In: KDD, pp 470-479. KDD, Portland
22. Moise G, Sander J (2008) Finding non-redundant, statistically significant regions in high dimensional data: A novel approach to projected and subspace clustering. In: KDD, pp 533-541. KDD, Portland
23. Moser F, Colak R, Rafiey A, Ester M (2009) Mining cohesive patterns from graphs with feature vectors. In: SDM, pp 593-604. SDM, Bethesda
24. Müller E, Assent I, Günnemann S, Krieger R, Seidl T (2009) Relevant subspace clustering: mining themost interesting non-redundant concepts in high dimensional data. In: ICDM, pp 377-386. ICDM, Sydney
25. Müller E, Günnemann S, Assent I, Seidl T (2009) Evaluating clustering in subspace projections of high dimensional data. In: VLDB, pp 1270-1281. VLDB, Singapore
26. Parsons L, Haque E, LiuH (2004) Subspace clustering for high dimensional data: A review. SIGKDD Explor 6(1):90-105
27. Pei J, Jiang D, Zhang A (2005) On mining cross-graph quasi-cliques. In: KDD, pp 228-238. KDD, Portland
28. Ruan J, ZhangW(2007) An efficient spectral algorithm for network community discovery and its applications to biological and social networks. In: ICDM, pp 643-648. ICDM, Sydney
29. Ulitsky I, ShamirR(2007) Identification of functionalmodules using network topology and high-throughput data. BMC Syst Biol 1(1):8
30. Zhou Y, Cheng H, Yu JX (2009) Graph clustering based on structural/attribute similarities. PVLDB 2(1):718-729
31. Zhou Y, Cheng H, Yu JX (2010) Clustering large attributed graphs: An efficient incremental approach. In: ICDM, pp 689-698. ICDM, Sydney