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Hidetomo Ichihashi - One of the best experts on this subject based on the ideXlab platform.

  • FUZZ-IEEE - Cluster validation in linear fuzzy clustering of relational data from multi-cluster Principal Coordinate Analysis view point
    2009 IEEE International Conference on Fuzzy Systems, 2009
    Co-Authors: Naoki Haga, Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi
    Abstract:

    This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model can be regarded as a multi-cluster MDS model. In the new cluster validation approach, the quality of fuzzy partitions is measured from the multi-cluster Principal Coordinate Analysis view point, in which the reconstructed low dimensional substructure in each cluster is compared with the result of Principal Coordinate Analysis considering fuzzy membership degrees to the cluster.

  • cluster validation in linear fuzzy clustering of relational data from multi cluster Principal Coordinate Analysis view point
    IEEE International Conference on Fuzzy Systems, 2009
    Co-Authors: Naoki Haga, Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi
    Abstract:

    This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model can be regarded as a multi-cluster MDS model. In the new cluster validation approach, the quality of fuzzy partitions is measured from the multi-cluster Principal Coordinate Analysis view point, in which the reconstructed low dimensional substructure in each cluster is compared with the result of Principal Coordinate Analysis considering fuzzy membership degrees to the cluster.

  • fuzzy Principal Coordinate Analysis
    한국지능시스템학회 국제학술대회 발표논문집, 2009
    Co-Authors: Naoki Haga, Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi
    Abstract:

    Principal Coordinate Analysis (PCO or PCoA) is a classical (metric) multi-dimensional scaling technique and is regarded as a Principal component Analysis (PCA) technique for relational data because PCO is equivalent to PCA on covariance matrix of transposed data matrix, when the mutual relations among samples are given by Euclidean distances. This paper proposes a fuzzy version of PCO in order to reveal the intrinsic structure for relational data sets. The proposed method introduces fuzzy memberships into PCO and is equivalent to fuzzy PCA if the mutual relations among samples are given by Euclidean distances. Therefore, it can be regarded to a fuzzy PCA technique for non-object-type data. In the algorithm, we apply symmetric dissimilarity matrices to centering for both of columns and rows in a fuzzy manner using fuzzy centers before the eigen decomposition.

Rahmatollah Karimizadeh - One of the best experts on this subject based on the ideXlab platform.

  • Use of Principal Coordinate Analysis for Measuring GE Interactions in Rain-Fed Durum Wheat Genotypes
    Journal of Agricultural Sciences, 2019
    Co-Authors: Rahmatollah Karimizadeh, Ali Asghari, Rahim Chinipardaz, Omid Sofalian, Abdolali Ghaffari, Kamal Shahbazi, T Hosseinpour, Hassan Ghojog, Mohammad Armion
    Abstract:

    Genotype × environment interactions complicate selection of superior genotypes for narrow and wide adaptation. Multienvironment yield trials of twenty durum wheat genotypes were conducted at five locations of Iran (Gachsaran, Gonbad, Moghan, Ilam and Khorram abad) over four years (2009-2013). Combined ANOVA of yield data of the twenty environments (year/location combined) revealed highly significant differences among genotypes and environments as well as significant genotype-environment interaction indicated differential performance of genotypes over test environments. The GE interaction was examined using multivariate Analysis technique as Principal Coordinate Analysis (PCOA). According to grand means and total mean yield, test environments were grouped into two main groups as high mean yield (H) and low mean yield (L). There were eleven H test environments and nine L test environments which analyzed in the sequential cycles. For each cycle, both scatter point diagram and minimum spanning tree plot were drawn. The identified most stable genotypes with dynamic stability concept and based on the minimum spanning tree plots and centroid distances were G12 (3342 kg ha-1), G10 (3470.3 kg ha-1), G5 (3203.0 kg ha-1), and G1 (3263.5 kg ha-1), and therefore could be recommended for unfavorable or poor conditions. Genotypes G10 (3470.3 kg ha-1) and G9 (3404.2 kg ha-1) were located several times in the vertex positions of high cycles according to the Principal Coordinates Analysis (PCOA) and therefore could be recommended for favorable or rich conditions. Finally, the results of Principal Coordinates Analysis in general confirmed the breeding value of the genotypes, obtained on the basis of the yield stability evaluation.

  • Principal Coordinate Analysis of genotype × environment interaction for grain yield of bread wheat in the semi-arid regions
    Genetika-belgrade, 2013
    Co-Authors: Naser Sabaghnia, Mohtasham Mohammadi, Rahmatollah Karimizadeh
    Abstract:

    Multi-environmental trials have significant main effects and significant multiplicative genotype × environment (GE) interaction effect. Principal Coordinate Analysis (PCOA) offers a more appropriate statistical Analysis to deal with such situations, compared to traditional statistical methods. Eighteen bread wheat genotypes were grown in four semi-arid regions over three year seasons to study the GE interaction and yield stability and obtained data on grain yield were analyzed using PCOA. Combined Analysis of variance indicated that all of the studied effects including the main effects of genotype and environments as well as the GE interaction were highly significant. According to grand means and total mean yield, test environments were grouped to two main groups as high mean yield (H) and low mean yield (L). There were five H test environments and six L test environments which analyzed in the sequential cycles. For each cycle, both scatter point diagram and minimum spanning tree plot were drawn. The identified most stable genotypes with dynamic stability concept and based on the minimum spanning tree plots and centroid distances were G1 (3310.2 kg ha-1) and G5 (3065.6 kg ha-1), and therefore could be recommended for unfavorable or poor conditions. Also, genotypes G7 (3047.2 kg ha-1) and G16 (3132.3 kg ha-1) were located several times in the vertex positions of high cycles according to the Principal Coordinates Analysis. The Principal Coordinates Analysis provided useful and interesting ways of investigating GE interaction of barley genotypes. Finally, the results of Principal Coordinates Analysis in general confirmed the breeding value of the genotypes, obtained on the basis of the yield stability evaluation.

  • Principal Coordinate Analysis of genotype environment interaction for grain yield of bread wheat in the semi arid regions
    Genetika-belgrade, 2013
    Co-Authors: Naser Sabaghnia, Mohtasham Mohammadi, Rahmatollah Karimizadeh
    Abstract:

    Multi-environmental trials have significant main effects and significant multiplicative genotype × environment (GE) interaction effect. Principal Coordinate Analysis (PCOA) offers a more appropriate statistical Analysis to deal with such situations, compared to traditional statistical methods. Eighteen bread wheat genotypes were grown in four semi-arid regions over three year seasons to study the GE interaction and yield stability and obtained data on grain yield were analyzed using PCOA. Combined Analysis of variance indicated that all of the studied effects including the main effects of genotype and environments as well as the GE interaction were highly significant. According to grand means and total mean yield, test environments were grouped to two main groups as high mean yield (H) and low mean yield (L). There were five H test environments and six L test environments which analyzed in the sequential cycles. For each cycle, both scatter point diagram and minimum spanning tree plot were drawn. The identified most stable genotypes with dynamic stability concept and based on the minimum spanning tree plots and centroid distances were G1 (3310.2 kg ha-1) and G5 (3065.6 kg ha-1), and therefore could be recommended for unfavorable or poor conditions. Also, genotypes G7 (3047.2 kg ha-1) and G16 (3132.3 kg ha-1) were located several times in the vertex positions of high cycles according to the Principal Coordinates Analysis. The Principal Coordinates Analysis provided useful and interesting ways of investigating GE interaction of barley genotypes. Finally, the results of Principal Coordinates Analysis in general confirmed the breeding value of the genotypes, obtained on the basis of the yield stability evaluation.

Naoki Haga - One of the best experts on this subject based on the ideXlab platform.

  • FUZZ-IEEE - Cluster validation in linear fuzzy clustering of relational data from multi-cluster Principal Coordinate Analysis view point
    2009 IEEE International Conference on Fuzzy Systems, 2009
    Co-Authors: Naoki Haga, Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi
    Abstract:

    This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model can be regarded as a multi-cluster MDS model. In the new cluster validation approach, the quality of fuzzy partitions is measured from the multi-cluster Principal Coordinate Analysis view point, in which the reconstructed low dimensional substructure in each cluster is compared with the result of Principal Coordinate Analysis considering fuzzy membership degrees to the cluster.

  • cluster validation in linear fuzzy clustering of relational data from multi cluster Principal Coordinate Analysis view point
    IEEE International Conference on Fuzzy Systems, 2009
    Co-Authors: Naoki Haga, Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi
    Abstract:

    This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model can be regarded as a multi-cluster MDS model. In the new cluster validation approach, the quality of fuzzy partitions is measured from the multi-cluster Principal Coordinate Analysis view point, in which the reconstructed low dimensional substructure in each cluster is compared with the result of Principal Coordinate Analysis considering fuzzy membership degrees to the cluster.

  • fuzzy Principal Coordinate Analysis
    한국지능시스템학회 국제학술대회 발표논문집, 2009
    Co-Authors: Naoki Haga, Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi
    Abstract:

    Principal Coordinate Analysis (PCO or PCoA) is a classical (metric) multi-dimensional scaling technique and is regarded as a Principal component Analysis (PCA) technique for relational data because PCO is equivalent to PCA on covariance matrix of transposed data matrix, when the mutual relations among samples are given by Euclidean distances. This paper proposes a fuzzy version of PCO in order to reveal the intrinsic structure for relational data sets. The proposed method introduces fuzzy memberships into PCO and is equivalent to fuzzy PCA if the mutual relations among samples are given by Euclidean distances. Therefore, it can be regarded to a fuzzy PCA technique for non-object-type data. In the algorithm, we apply symmetric dissimilarity matrices to centering for both of columns and rows in a fuzzy manner using fuzzy centers before the eigen decomposition.

Sandrine Pavoine - One of the best experts on this subject based on the ideXlab platform.

  • Description of Species Structures
    Multivariate Analysis of Ecological Data with ade4, 2018
    Co-Authors: Jean Thioulouse, Stéphane Dray, Anne-béatrice Dufour, Aurélie Siberchicot, Thibaut Jombart, Sandrine Pavoine
    Abstract:

    Several simple data Analysis methods can be used to analyse species data tables, i.e., tables having sites as rows and species as columns. Like in the previous chapter, simple means that these methods are adapted to the Analysis of only one table. Three particular data Analysis methods will be studied here: Correspondence Analysis (CA), centred Principal Component Analysis (cPCA), and Principal Coordinate Analysis (PCoA).

  • Analysing Patterns of Biodiversity
    Multivariate Analysis of Ecological Data with ade4, 2018
    Co-Authors: Jean Thioulouse, Stéphane Dray, Anne-béatrice Dufour, Aurélie Siberchicot, Thibaut Jombart, Sandrine Pavoine
    Abstract:

    Patterns of functional or phylogenetic diversity among communities can be described thanks to the Double Principal Coordinate Analysis (DPCoA). This approach depicts differences among communities in low-dimensional plots and explains those differences by their species compositions and the functional or phylogenetic differences among species.

  • From dissimilarities among species to dissimilarities among communities: a double Principal Coordinate Analysis
    Journal of Theoretical Biology, 2004
    Co-Authors: Sandrine Pavoine, A-b Anne-béatrice Dufour, Daniel Chessel
    Abstract:

    This paper presents a new ordination method to compare several communities containing species that differ according to their taxonomic, morphological or biological features. The objective is first to find dissimilarities among communities from the knowledge about differences among their species, and second to describe these dissimilarities with regard to the feature diversity within communities. In 1986, Rao initiated a general framework for analysing the extent of the diversity. He defined a diversity coefficient called quadratic entropy and a dissimilarity coefficient and proposed a decomposition of this diversity coefficient in a way similar to ANOVA. Furthermore, Gower and Legendre (1986) built a weighted Principal Coordinate Analysis. Using the previous context, we propose a new method called the double Principal Coordinate Analysis (DPCoA) to analyse the relation between two kinds of data. The first contains differences among species (dissimilarity matrix); the second the species distribution among communities (abundance or presence/absence matrix). A multidimensional space assembling the species points and the community points is built. The species points define the original differences between species and the community points define the deduced differences between communities. Furthermore, this multidimensional space is linked with the diversity decomposition into between-community and within-community diversities. One looks for axes that provide a graphical ordination of the communities and project the species onto them. An illustration is proposed comparing bird communities which live in different areas under mediterranean bioclimates. Compared to some existing methods, the double Principal Coordinate Analysis can provide a typology of communities taking account of an abundance matrix and can include dissimilarities among species. Finally, we show that such an approach generalizes some of these methods and allows us to develop new analyses.

Akira Notsu - One of the best experts on this subject based on the ideXlab platform.

  • FUZZ-IEEE - Cluster validation in linear fuzzy clustering of relational data from multi-cluster Principal Coordinate Analysis view point
    2009 IEEE International Conference on Fuzzy Systems, 2009
    Co-Authors: Naoki Haga, Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi
    Abstract:

    This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model can be regarded as a multi-cluster MDS model. In the new cluster validation approach, the quality of fuzzy partitions is measured from the multi-cluster Principal Coordinate Analysis view point, in which the reconstructed low dimensional substructure in each cluster is compared with the result of Principal Coordinate Analysis considering fuzzy membership degrees to the cluster.

  • cluster validation in linear fuzzy clustering of relational data from multi cluster Principal Coordinate Analysis view point
    IEEE International Conference on Fuzzy Systems, 2009
    Co-Authors: Naoki Haga, Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi
    Abstract:

    This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model can be regarded as a multi-cluster MDS model. In the new cluster validation approach, the quality of fuzzy partitions is measured from the multi-cluster Principal Coordinate Analysis view point, in which the reconstructed low dimensional substructure in each cluster is compared with the result of Principal Coordinate Analysis considering fuzzy membership degrees to the cluster.

  • fuzzy Principal Coordinate Analysis
    한국지능시스템학회 국제학술대회 발표논문집, 2009
    Co-Authors: Naoki Haga, Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi
    Abstract:

    Principal Coordinate Analysis (PCO or PCoA) is a classical (metric) multi-dimensional scaling technique and is regarded as a Principal component Analysis (PCA) technique for relational data because PCO is equivalent to PCA on covariance matrix of transposed data matrix, when the mutual relations among samples are given by Euclidean distances. This paper proposes a fuzzy version of PCO in order to reveal the intrinsic structure for relational data sets. The proposed method introduces fuzzy memberships into PCO and is equivalent to fuzzy PCA if the mutual relations among samples are given by Euclidean distances. Therefore, it can be regarded to a fuzzy PCA technique for non-object-type data. In the algorithm, we apply symmetric dissimilarity matrices to centering for both of columns and rows in a fuzzy manner using fuzzy centers before the eigen decomposition.