A semi-supervised approach to projected clustering with applications to microarray data

Authors:
Kevin Y. Yip;Lin Cheung;David W. Cheung;Liping Jing;Michael K. Ng
Affiliations:
Department of Computer Science, Yale University, New Haven, Connecticut, USA.;Department of Computer Science, The University of Hong Kong, Hong Kong.;Department of Computer Science, The University of Hong Kong, Hong Kong.;Center for Mathematical Imaging and Vision, Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.;Center for Mathematical Imaging and Vision, Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2009

Citing 20
Cited 2

C4.5: programs for machine learning

C4.5: programs for machine learning
Learning from a mixture of labeled and unlabeled examples with parametric side information

COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Automatic subspace clustering of high dimensional data for data mining applications

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Fast algorithms for projected clustering

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Finding generalized projected clusters in high dimensional spaces

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Clustering by pattern similarity in large data sets

Proceedings of the 2002 ACM SIGMOD international conference on Management of data
A Monte Carlo algorithm for fast projective clustering

Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Constrained K-means Clustering with Background Knowledge

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Clustering with Instance-level Constraints

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Biclustering of Expression Data

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Efficient and Effective Clustering Methods for Spatial Data Mining

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Machine learning methods applied to DNA microarray data can improve the diagnosis of cancer

ACM SIGKDD Explorations Newsletter
A probabilistic framework for semi-supervised clustering

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering

ICML '04 Proceedings of the twenty-first international conference on Machine learning
HARP: A Practical Projected Clustering Algorithm

IEEE Transactions on Knowledge and Data Engineering
A framework for projected clustering of high dimensional data streams

VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30

WF-MSB: A weighted fuzzy-based biclustering method for gene expression data

International Journal of Data Mining and Bioinformatics
Semi-supervised clustering algorithm for haplotype assembly problem based on MEC model

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

A clustering algorithm is introduced that combines the strengths of clustering and motif finding techniques. Clusters are identified based on unambiguously defined sequence sections as in motif finding algorithms. The definition of similarity within ...