The University of Hong Kong Department of Computer Science and Information System |
Field | Title | Authors | Appeared in | Year |
8 Feb 2002: Several Approaches to Improving the Performance of Classifying Very Large Datasets | ||||
Classification | An Interval Classifier for Database Mining Applications |
R. Agrawal, S. Ghosh, T. Imielinski, B. Iyer, A. Swami |
VLDB | 1992 |
Classification | SLIQ: A Fast Scalable Classifier for Data Mining |
M. Mehta, R. Agrawal, J. Rissanen |
EDBT | 1996 |
Classification | SPRINT: A Scalable Parallel Classifier for Data Mining |
J. C. Shafer, R. Agrawal, M. Mehta |
VLDB | 1996 |
Classification | Decision Tables: Scalable Classification Exploring RDBMS Capabilities |
Hongjun Lu, Hongyan Liu |
VLDB | 2000 |
17 May 2002: The Subspace Clustering Problem | ||||
Grid-Based Dimension Selection | Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications |
R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan |
SIGMOD | 1998 |
Grid-Based Dimension Selection | Entropy-based Subspace Clustering for Mining Numerical Data |
C. H. Cheng, A. W. C. Fu, Y. Zhang |
SIGKDD | 1999 |
Grid-Based Dimension Selection | MAFIA: Efficient and Scalable Subspace Clustering for Very Large Data Sets |
S. Goil, H. Nagesh, A. Choudhary |
Technical Report 9906-010, Northwestern University | 1999 |
Association Rule Hypergaph Partitioning | Clustering based on Association Rule Hypergraphs |
E. H. Han, G. Karypis, V. Kumar, B. Mobasher |
DAC | 1997 |
Association Rule Hypergaph Partitioning | Multilevel Hypergraph Partitioning: Application in VLSI Domain |
G. Karypis, R. Aggarwal, V. Kumar, S. Shekhar |
DAC | 1997 |
Context-Specific Bayesian Clustering | Context-Specific Bayesian Clustering for Gene Expression Data |
Y. Barash, N. Friedman |
RECOMB | 2001 |
Projected Clustering | Fast Algorithms for Projected Clustering |
C. C. Aggarwal, C. Procopiuc, J. L. Wolf, P. S. Yu, J. S. Park |
SIGMOD | 1999 |
Projected Clustering | Finding Generalized Projected Clusters in High Dimensional Spaces |
C. C. Aggarwal, P. S. Yu |
SIGMOD | 2000 |
Projected Clustering | A Monte Carlo Algorithm for Fast Projective Clustering |
C. M. Procopiuc, M. Jones, P. K. Agarwal, T. M. Murali |
SIGMOD | 2002 |
20 Sep 2002: HARP: A Hierarchical Approach with Automatic Relevant Attribute Selection for Projected Clustering | ||||
Projected Clustering | Fast Algorithms for Projected Clustering |
C. C. Aggarwal, C. Procopiuc, J. L. Wolf, P. S. Yu, J. S. Park |
SIGMOD | 1999 |
Projected Clustering | Finding Generalized Projected Clusters in High Dimensional Spaces |
C. C. Aggarwal, P. S. Yu |
SIGMOD | 2000 |
Projected Clustering | Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications |
R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan |
SIGMOD | 1998 |
Genetics | Distinct Types of Diffuse Large B-Cell Lymphoma Identified by Gene Expression Profiling |
A. A. Alizadeh, M. B. Eisen, R. E. Davis, C. Ma, I. S. Lossos, A. Rosenwald, J. C. Boldrick, H. Sabet, T. Tran, X. Yu, J. I. Powell, L. Yang, G. E. Marti, T. Moore, J. Hudson, L. Lu, D. B. Lewis, R. Tibshirani, G. Sherlock, W. C. Chan, T. C. Greiner, D. D.Weisenburger, J. O. Armitage, R. Warnke, R. Levy, W. Wilson, M. R. Grever, J. C. Byrd, D. Botstein, P. O. Brown, L. M. Staudt |
Nature | 2000 |
Context-Specific Bayesian Clustering | Context-Specific Bayesian Clustering for Gene Expression Data |
Y. Barash, N. Friedman |
RECOMB | 2001 |
Grid-Based Dimension Selection | Entropy-Based Subspace Clustering for Mining Numerical Data |
C. H. Cheng, A. W.-C. Fu, Y. Zhang |
SIGKDD | 1999 |
Clustering | ROCK: A Robust Clustering Algorithm for Categorical Attributes |
S. Guha, R. Rastogi, K. Shim |
ICDE | 1999 |
Association Rule Hypergaph Partitioning | Clustering based on Association Rule Hypergraphs |
E.-H. Han, G. Karypis, V. Kumar, B. Mobasher |
SIGMOD | 1997 |
Association Rule Hypergaph Partitioning | Multilevel Hypergraph Partitioning: Application in VLSI Domain |
G. Karypis, R. Aggarwal, V. Kumar, S. Shekhar |
DAC | 1997 |
Grid-Based Dimension Selection | MAFIA: Efficient and Scalable Subspace Clustering for Very Large Data Sets |
H. Nagesh, S. Goil, A. Choudhary |
Technical Report 9906-010, Northwestern University | 1999 |
Projected Clustering | A Monte Carlo Algorithm for Fast Projective Clustering |
C. M. Procopiuc, M. Jones, P. K. Agarwal, T. M. Murali |
SIGMOD | 2002 |
Pattern Similarity | Clustering by Pattern Similarity in Large Data Sets |
H. Wang, W. Wang, J. Yang, P. S. Yu |
SIGMOD | 2002 |
26 Mar 2003: Validation and Presentation of Clustering Results | ||||
Internal Validation | Merging of Distance Matrices and Classification by Dynamic Clustering |
Marie-Odile Delorme, Alain Henaut |
CABIOS vol. 4, no. 4 | 1988 |
Internal Validation | CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling |
George Karypis, Eui-Hong (Sam) Han, Vipin Kumar |
IEEE Computer vol. 32, no. 8, p.68-75 | 1999 |
Internal Validation | An Empirical Study on the Visual Cluster Validation Method with Fastmap |
Zhexue Huang, David W. Cheung, Michael K. Ng |
DASFAA | 2001 |
Internal Validation | Validating Clustering for Gene Expression Data |
Ka Yee Yeung, David R. Haynor Walter L. Ruzzo |
Bioinformatics vol. 17, no. 4 | 2001 |
Internal Validation | Comparisons and Validation of Statistical Clustering Techniques for Microarray Gene Expression Data |
Susmita Datta, Somnath Datta |
Bioinformatics vol. 19, no. 4 | 2003 |
Result Presentation | Cluster Analysis and Display of Genome-wide Expression Patterns | Michael B. Eisen et al. | Proc. Natl. Acad. Sci, USA vol. 95 | 1998 |
Result Presentation | OPTICS: Ordering Points To Identify the Clustering Structure | Michael Anakerst et al. | SIGMOD | 1999 |
Result Presentation | Distinct Types of Diffuse Large B-cell Lymphoma Identified by Gene Expression Profiling | Ash A. Alizadeh et al. | Nature vol. 403 | 2000 |
Result Presentation | An Empirical Study of Principal Component Analysis for Clustering Gene Expression Data |
K. Y. Yeung, W. L. Ruzzo |
Bioinformatics, vol. 17, no. 9 | 2001 |
Result Presentation | Fast Optimal Leaf Ordering for Hierarchical Clustering |
Ziv Bar-Joseph, David K. Gifford, Tommi S. Jaakkola |
Bioinformatics, vol. 17, suppl. 1 | 2001 |
Result Presentation | Hierarchical Cluster Analysis of SAGE Data for Cancer Profiling |
Raymond T. Ng, Jörg Sander, Monica C. Sleumer |
BIOKDD | 2001 |
10 Sep 2003: Biclustering Methods for Microarray Data Analysis | ||||
Biclustering | Biclustering of Expression Data |
Yizong Cheng, George M. Church |
ISMB | 2000 |
Biclustering | Coupled Two-Way Clustering Analysis of Gene Microarray Data |
G. Getz, E. Levine, E. Domany |
Proc. Natl. Acad. Sci. USA | 2000 |
Biclustering | Plaid Models for Gene Expression Data |
Laura Lazzeroni, Art Owen |
Statistica Sinica | 2002 |
Biclustering | Discovering Local Structure in Gene Expression Data: The Order-Preserving Submatrix Problem |
Amir Ben-Dor, Benny Chor, Richard Karp, Zohar Yakhini |
RECOMB | 2002 |
Biclustering | Discovering Statistically Significant Biclusters in Gene Expression Data |
Amos Tanay, Roded Sharan, Ron Shamir |
Bioinformatics | 2002 |
Biclustering | Enhanced Biclustering on Expression Data |
Jiong Yang, Haixun Wang, Wei Wang, Philip Yu |
BIBE | 2003 |
Biclustering | Spectral Biclustering of Microarray Cancer Data: Co-clustering Genes and Conditions |
Yuval Kluger, Ronen Basri, Joseph T. Chang, Mark Gerstein |
Genome Res. | 2003 |
Back to the top |
Comment? Send to dbgroup@csis.hku.hk |