A Memetic Heuristic for the Co-clustering Problem

Mohammad Khoshneshin; Mahtab Ghazizadeh; W. Nick Street; Jeffrey Ohlmann

8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

Research Article

A Memetic Heuristic for the Co-clustering Problem

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/icst.bict.2014.257884,
    author={Mohammad Khoshneshin and Mahtab Ghazizadeh and W. Nick Street and Jeffrey Ohlmann},
    title={A Memetic Heuristic for the Co-clustering Problem},
    proceedings={8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)},
    publisher={ICST},
    proceedings_a={BICT},
    year={2015},
    month={2},
    keywords={co-clustering bregman co-clustering memetic algorithm},
    doi={10.4108/icst.bict.2014.257884}
}

Mohammad Khoshneshin
Mahtab Ghazizadeh
W. Nick Street
Jeffrey Ohlmann
Year: 2015
A Memetic Heuristic for the Co-clustering Problem
BICT
ACM
DOI: 10.4108/icst.bict.2014.257884

Mohammad Khoshneshin¹^,*, Mahtab Ghazizadeh², W. Nick Street¹, Jeffrey Ohlmann¹

1: University of Iowa
2: University of Wisconsin-Madison

*Contact email: mohammad-khoshneshin@uiowa.edu

Abstract

Co-clustering partitions two different kinds of objects simultaneously. Bregman co-clustering is a well-studied fast iterative algorithm to perform co-clustering. However, this method is very prone to local optima. We propose a memetic algorithm to solve the co-clustering problem. Experimental results show that this method outperforms the multi-start Bregman co-clustering in both accuracy and time.

Keywords: co-clustering bregman co-clustering memetic algorithm

Published: 2015-02-02
Publisher: ICST
Appears in: ACM Digital Library

: http://dx.doi.org/10.4108/icst.bict.2014.257884