An efficient greedy K-means algorithm for global gene trajectory clustering

Authors:
Zeke S. H. Chan;Lesley Collins;N. Kasabov
Affiliations:
Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Auckland, New Zealand;Allan Wilson Center for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand;Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Auckland, New Zealand
Venue:
Expert Systems with Applications: An International Journal
Year:
2006

Citing 5
Cited 5

Segmentation of stock trading customers according to potential value

Expert Systems with Applications: An International Journal
A hybrid system combining self-organizing maps with case-based reasoning in wholesaler's new-release book forecasting

Expert Systems with Applications: An International Journal
Developing a diagnostic system through integration of fuzzy case-based reasoning and fuzzy ant colony system

Expert Systems with Applications: An International Journal
Hybrid mining approach in the design of credit scoring models

Expert Systems with Applications: An International Journal
A case-based reasoning system for PCB defect prediction

Expert Systems with Applications: An International Journal

Estimating null values in relational database systems using automatic clustering and multiple regression techniques

Expert Systems with Applications: An International Journal
Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms

Expert Systems with Applications: An International Journal
OWA-based linkage method in hierarchical clustering: Application on phylogenetic trees

Expert Systems with Applications: An International Journal
Combining two data mining methods for system identification

EG-ICE'06 Proceedings of the 13th international conference on Intelligent Computing in Engineering and Architecture
Multi-stage filtering for improving confidence level and determining dominant clusters in clustering algorithms of gene expression data

Computers in Biology and Medicine

Quantified Score

Hi-index	12.06

Visualization

Abstract

Optimal clustering of co-regulated genes is critical for reliable inference of the underlying biological processes in gene expression analysis, for which the K-means algorithm have been widely employed for its efficiency. However, given that the solution space is large and multimodal, which is typical of gene expression data, K-means is prone to produce inconsistent and sub-optimal cluster solutions that may be unreliable and misleading for biological interpretation. This paper applies a novel global clustering method called the greedy elimination method (GEM) to alleviate these problems. GEM is simple to implement, yet very effective in improving the global optimality of the solutions. Experiments over two sets of gene expression data show that the GEM scores significantly lower clustering errors than the standard K-means and the greedy incremental method.