Displaying similar documents to “On the similarity of sets”

An n-ary λ-averaging based similarity classifier

Onesfole Kurama, Pasi Luukka, Mikael Collan (2016)

International Journal of Applied Mathematics and Computer Science

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We introduce a new n-ary λ similarity classifier that is based on a new n-ary λ-averaging operator in the aggregation of similarities. This work is a natural extension of earlier research on similarity based classification in which aggregation is commonly performed by using the OWA-operator. So far λ-averaging has been used only in binary aggregation. Here the λ-averaging operator is extended to the n-ary aggregation case by using t-norms and t-conorms. We examine four different n-ary...

Analysis of the relationship between coefficients of relatedness and molecular similarity of parental forms with respect to the heterosis effect in maize

Agnieszka Tomkowiak, Zbigniew Broda, Krzysztof Moliński, Marta Molińska-Glura, Józef Adamczyk (2013)

Biometrical Letters

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Within the last twenty years studies have been conducted at many research centers with the aim of dividing breeding materials into heterotic groups based on molecular markers. Molecular techniques make it possible to study the genetic purity of inbred lines, determine their genetic variability and classify breeding materials for which no information is available on their origin. This study aims to investigate relationships between coefficients of relatedness (pedigree analysis) and molecular...

Correspondence analysis and two-way clustering.

Antonio Ciampi, Ana González Marcos, Manuel Castejón Limas (2005)

SORT

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Correspondence analysis followed by clustering of both rows and columns of a data matrix is proposed as an approach to two-way clustering. The novelty of this contribution consists of: i) proposing a simple method for the selecting of the number of axes; ii) visualizing the data matrix as is done in micro-array analysis; iii) enhancing this representation by emphasizing those variables and those individuals which are 'well represented' in the subspace of the chosen axes. The approach...

Analysis of correlation based dimension reduction methods

Yong Joon Shin, Cheong Hee Park (2011)

International Journal of Applied Mathematics and Computer Science

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Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are different in that DCCA is a supervised method utilizing class label information, while CCA is an unsupervised...