Recent advances in cluster analysis

Xu, Rui; Wunsch, Donald C.

doi:10.1108/17563780810919087

Cited by 29 publications

(23 citation statements)

References 67 publications

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“…One approach that may facilitate interpretation is to apply clustering analysis on GEI in order to group genotypes and/or environments with similar genotype-environment interactions. With groups at hand, one option for a cluster representative which is free from possible outlier influence is the medoid, that is, the genotype that is most similar to each other genotype in the group on average (Xu and Wunsch, 2008).…”

Section: Introductionmentioning

confidence: 99%

AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data

Neisse

Kirch

Hongyu

2018

Biometrical Letters

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The presence of genotype-environment interaction (GEI) influences production making the selection of cultivars in a complex process. The two most used methods to analyze GEI and evaluate genotypes are AMMI and GGE Biplot, being used for the analysis of multi environment trials data (MET). Despite their different approaches, both models complement each other in order to strengthen decision making. However, both models are based on biplots, consequently, biplot-based interpretation doesn't scale well beyond two-dimensional plots, which happens whenever the first two components don't capture enough variation. This paper proposes an approach to such cases based on cluster analysis combined with the concept of medoids. It also applies AMMI and GGE Biplot to the adjusted data in order to compare both models. The data is provided by the International Maize and Wheat Improvement Center (CIMMYT) and comes from the 14th Semi-Arid Wheat Yield Trial (SAWYT), an experiment concerning 50 genotypes of spring bread wheat (Triticum aestivum) germplasm adapted to low rainfall. It was performed in 36 environments across 14 countries. The analysis provided 25 genotypes clusters and 6 environments clusters. Both models were equivalent for the data's evaluation, permitting increased reliability in the selection of superior cultivars and test environments.Key words: genotype × environment interaction, adaptability and stability, additive main effects and multiplicative interaction model, multienvironment trials, cluster analysis, medoids

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Section: Introductionmentioning

confidence: 99%

AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data

Neisse

Kirch

Hongyu

2018

Biometrical Letters

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“…Given a set of data objects (also known as patterns, entities, instances, observances, or units), cluster analysis aims to explore natural and hidden data structure and to provide insights to the questions such as, "Are there any clusters (groups, subsets, or categories) in the data, and if yes, how many clusters are in the data? (Xi & Wunsch II, 2008).…”

Section: Findings and Discussionmentioning

confidence: 99%

Dimensions of Decision-Making Process Quality and Company Performance: A Study of Top Managers in Slovenia

Grušovnik¹,

Kavkler

Uršič

2017

Naše Gospodarstvo/Our Economy

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This paper investigates the relationship between the dimensions of the decisionmaking process quality and company performance of top managers' in Slovenia. We found out that companies whose managers exhibit an above-average dimension of openness of spirit in the quality of the decision making process, on average, have a higher stance on foreign markets as companies in which managers show a below-average open spirit. For the managers who work in companies that are present in foreign markets, we could confirm that there is a low/weak correlation between the dimension of effort of the decision-making process quality and the number of employees in a company.

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“…A good survey of recent advances in cluster analysis can be found in Xu and Wunsch (2008). Reducing the dimensionality of data using CD is computationally less complex than SVD.…”

Section: Introductionmentioning

confidence: 99%

Reducing data dimensionality using random projections and fuzzy k‐means clustering

Cherukuri

2011

International Journal of Intelligent Computing and Cybernetics

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Purpose -The purpose of this paper is to introduce a new hybrid method for reducing dimensionality of high dimensional data. Design/methodology/approach -Literature on dimensionality reduction (DR) witnesses the research efforts that combine random projections (RP) and singular value decomposition (SVD) so as to derive the benefit of both of these methods. However, SVD is well known for its computational complexity. Clustering under the notion of concept decomposition is proved to be less computationally complex than SVD and useful for DR. The method proposed in this paper combines RP and fuzzy k-means clustering (FKM) for reducing dimensionality of the data. Findings -The proposed RP-FKM is computationally less complex than SVD, RP-SVD. On the image data, the proposed RP-FKM has produced less amount of distortion when compared with RP. The proposed RP-FKM provides better text retrieval results when compared with conventional RP and performs similar to RP-SVD. For the text retrieval task, superiority of SVD over other DR methods noted here is in good agreement with the analysis reported by Moravec. Originality/value -The hybrid method proposed in this paper, combining RP and FKM, is new. Experimental results indicate that the proposed method is useful for reducing dimensionality of high-dimensional data such as images, text, etc.

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Recent advances in cluster analysis

Cited by 29 publications

References 67 publications

AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data

AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data

Dimensions of Decision-Making Process Quality and Company Performance: A Study of Top Managers in Slovenia

Reducing data dimensionality using random projections and fuzzy k‐means clustering

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