2009•Unpublished venueRequires access

Alternative hierarchical clustering approach in construction of phylogenetic trees

Çağın Kandemir Çavaş, Efendi N. Nasibov

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Abstract

Hierarchical clustering algorithms are frequently used in constructing phylogenetic trees of protein sequences. Hierarchical clustering is the recursive clustering of data points. Similarity is computed by measuring the distance between elements. Relationship between data is visually denoted by dendrogram. Single linkage, average linkage, complete linkage and Ward's linkage are some of frequently used hierarchical clustering algorithms. In this study, an alternative solution based on Ordered Weighted Average (OWA) aggregation operator is proposed in solving distance between clusters in hierarchical clustering of proteins.

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What this paper is about

Hierarchical clustering algorithms are frequently used in constructing phylogenetic trees of protein sequences. Hierarchical clustering is the recursive clustering of data points. Similarity is computed by measuring the distance between elements. Relationship between data is visually denoted by dendrogram. Single linkage, average linkage, complete linkage and Ward's linkage are some of frequently used hierarchical clustering algorithms. In this study, an alternative solution based on Ordered Weighted Average (OWA) aggregation operator is proposed in solving distance between clusters in hierarchical clustering of proteins.

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Available abstract

Hierarchical clustering algorithms are frequently used in constructing phylogenetic trees of protein sequences. Hierarchical clustering is the recursive clustering of data points. Similarity is computed by measuring the distance between elements. Relationship between data is visually denoted by dendrogram. Single linkage, average linkage, complete linkage and Ward's linkage are some of frequently used hierarchical clustering algorithms. In this study, an alternative solution based on Ordered Weighted Average (OWA) aggregation operator is proposed in solving distance between clusters in hierarchical clustering of proteins.

Key concepts: Cluster analysis, Dendrogram, Hierarchical clustering, Single-linkage clustering, Hierarchical clustering of networks, Complete-linkage clustering, Complete linkage, Similarity (geometry)

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