Next Article in Journal
A Facility Layout Problem in a Marble Factory via Simulation
Previous Article in Journal
Optimization of Open Canal Cross Sections by Differential Evolution Algorithm
Article Menu

Article Versions

Export Article

Open AccessArticle
Math. Comput. Appl. 2011, 16(1), 87-96; doi:10.3390/mca16010087

Performance of an Ensemble Clustering Algorithm on Biological Data Sets

1
Industrial and Systems Engineering Department, Mississippi State University, 39762, USA
2
Computer Science and Engineering Department, Mississippi State University, 39762, USA
3
University, Electrical and Electronics Engineering, 46100, Turkey
*
Author to whom correspondence should be addressed.
Published: 1 April 2011
Download PDF [166 KB, uploaded 5 April 2016]

Abstract

Ensemble clustering is a promising approach that combines the results of multiple clustering algorithms to obtain a consensus partition by merging different partitions based upon well-defined rules. In this study, we use an ensemble clustering approach for merging the results of five different clustering algorithms that are sometimes used in bioinformatics applications. The ensemble clustering result is tested on microarray data sets and compared with the results of the individual algorithms. An external cluster validation index, adjusted rand index (C-rand), and two internal cluster validation indices; silhouette, and modularity are used for comparison purposes.
Keywords: Ensemble Clustering; Rand Index; Silhouette Index; Modularity; Microarray Data Sets Ensemble Clustering; Rand Index; Silhouette Index; Modularity; Microarray Data Sets
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Pirim, H.; Gautam, D.; Bhowmik, T.; Perkins, A.D.; Ekşioglu, B.; Alkan, A. Performance of an Ensemble Clustering Algorithm on Biological Data Sets. Math. Comput. Appl. 2011, 16, 87-96.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Math. Comput. Appl. EISSN 2297-8747 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top