Message Passing Clustering (MPC): a knowledge-based framework for clustering under biological constraints

Authors:
Huimin Geng;Xutao Deng;Hesham H. Ali
Affiliations:
Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182, USA.;Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182, USA.;Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2008

Citing 19
Cited 3

Algorithms for clustering data

Algorithms for clustering data
Efficiency of hierarchic agglomerative clustering using the ICL distributed array processor

Journal of Documentation
A parallel bottom-up clustering algorithm with applications to circuit partitioning in VLSI design

DAC '93 Proceedings of the 30th international Design Automation Conference
Self-organizing maps

Self-organizing maps
Simulation

Simulation
Clustering Algorithms

Clustering Algorithms
A Fast Parallel Clustering Algorithm for Large Spatial Databases

Data Mining and Knowledge Discovery
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
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets

ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
Gene Ontology Friendly Biclustering of Expression Profiles

CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A New Approach to Clustering Biological Data Using Message Passing

CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Semi-supervised protein classification using cluster kernels

Bioinformatics
A New Clustering Strategy with Stochastic Merging and Removing Based on Kernel Functions

CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
On Clustering Biological Data Using Unsupervised and Semi-Supervised Message Passing

BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Message Passing Clustering (MPC): a knowledge-based framework for clustering under biological constraints

International Journal of Data Mining and Bioinformatics
Cluster Analysis

Cluster Analysis

Message Passing Clustering (MPC): a knowledge-based framework for clustering under biological constraints

International Journal of Data Mining and Bioinformatics
Detecting microarray data supported microRNA-mRNA interactions

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 new clustering algorithm, Message Passing Clustering (MPC), is proposed. MPC employs the concept of message passing to describe parallel and spontaneous clustering process by allowing data objects to communicate with each other. MPC also provides an extensible framework to accommodate additional features into clustering, such as adaptive feature weights scaling, stochastic cluster merging, and semi-supervised constraints guiding. Extensive experiments were performed using both simulation and real microarray gene expression and phylogenetic data. The results showed that MPC performed favourably to other popular clustering algorithms and MPC with the integration of additional features gave even higher accuracy rate than MPC.