Unsupervised hierarchical clustering via a genetic algorithm

Greene, William A.

doi:10.1109/cec.2003.1299776

Cited by 11 publications

(26 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, the computational complexity of this algorithm grows exponentially for large data sets, as is the case of DNA microarray data. The method of Greene (2003) has the drawback of having a nominal scale (non-real values) on both, data set and proximity matrix, which restricts its use. Moreover, the approach of centroid computation for hierarchical clustering can imply a time overload.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, both works (Lozano and Larrañaga, 1999) and (Greene, 2003) carried out for other data contexts very different of gene expression data, and neither of them, introduce heuristics to reduce the complexity of the search space. According to latter, the first experiments carried out on HCGA without including constraints to the search space (as made in Lozano and Larrañaga (1999) and Greene (2003)), showed that HCGA is intractable according to runtime when the data set is large. This suggests that the GA-based search for an optimum dendrogram without including constraints still remains intractable, in general.…”

Section: Related Workmentioning

confidence: 99%

“…In Greene (2003) a hierarchical, incremental, and unsupervised clustering algorithm was defined. This algorithm is based on the work performed in Greene (2001), and it is distance-based.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

An evolutionary computational model applied to cluster analysis of DNA microarray data

Castellanos-Garzón

Díaz

2013

Expert Systems with Applications

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An evolutionary computational model applied to cluster analysis of DNA microarray data

Castellanos-Garzón

Díaz

2013

Expert Systems with Applications

View full text Add to dashboard Cite

“…In this work, we have used GAs as a function optimization problem to evolve structures representing sets of acoustic segments rules as used in Hansohm (2000), Aissiou and Guerti (2004), Greene (2003).…”

Section: Introductionmentioning

confidence: 99%

Genetic supervised classification of Standard Arabic fricative sounds

Aissiou

Guerti

2009

Int J Speech Technol

View full text Add to dashboard Cite

The purpose of this paper is the application of the Genetic Algorithms (GAs) to the supervised classification level, in order to recognize Standard Arabic (SA) fricative consonants of continuous, naturally spoken, speech. We have used GAs because of their advantages in resolving complicated optimization problems where analytic methods fail. For that, we have analyzed a corpus that contains several sentences composed of the thirteen types of fricative consonants in the initial, medium and final positions, recorded by several male Jordanian speakers. Nearly all the world's languages contain at least one fricative sound. The SA language occupies a rather exceptional position in that nearly half of its consonants are fricatives and nearly half of fricative inventory is situated far back in the uvular, pharyngeal and glottal areas. We have used Mel-Frequency Cepstral analysis method to extract vocal tract coefficients from the speech signal. Among a set of classifiers like Bayesian, likelihood and distance classifier, we have used the distance one. It is based on the classification measure criterion. So, we formulate the supervised classification as a function optimization problem and we have used the decision rule Mahalanobis distance as the fitness function for the GA evaluation. We report promising results with a classification recognition accuracy of 82%.M. Aissiou ( ) · M. Guerti Algiers National Polytechnique School, BP 182, El-Harrach,

show abstract

“…Greene [12] proposes a method that generates hierarchies of partitions. It begins with a top-down method by which the initial population is subdivided into several subpopulations.…”

Section: B Genetic Operatorsmentioning

confidence: 99%

Evolutionary Method of Population Classification According to Level of Social Resilience

Tiekoura¹,

Marcellin²,

Michel³

et al. 2017

ijacsa

View full text Add to dashboard Cite

Abstract-Following the many natural disasters and global socio-economic upheavals of the 21st century, the concept of resilience is increasingly the subject of much research aimed at finding appropriate responses to these traumas. However, most existing work on resilience is limited to a broad cross-disciplinary panel of non-operational theoretical approaches. Thus, the study of the processes of social resilience is confronted with difficulties of modeling and a lack of appropriate analysis tools. However, the existing stratification methods are too general to take into account the specificities of the resilience and are difficult to use for non-specialists in modeling. In addition, most traditional methods of partition research have limitations including their inability to effectively exploit the research space. In this paper, we propose a classification algorithm based on the technique of genetic algorithms and adapted to the context of social resilience. Our objective function, after penalization by two criteria, allows to explore widely the space of research for solutions while favoring classes quite homogeneous and well separated between them.

show abstract

Unsupervised hierarchical clustering via a genetic algorithm

Cited by 11 publications

References 21 publications

An evolutionary computational model applied to cluster analysis of DNA microarray data

An evolutionary computational model applied to cluster analysis of DNA microarray data

Genetic supervised classification of Standard Arabic fricative sounds

Evolutionary Method of Population Classification According to Level of Social Resilience

Contact Info

Product

Resources

About