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Laplacian normalization for deriving thematic fuzzy clusters with an additive spectral approach

Expert Systems, 2013
AbstractThis paper presents a further investigation into computational properties of a novel fuzzy additive spectral clustering method, Fuzzy Additive Spectral clustering (FADDIS), recently introduced by authors. Specifically, we extend our analysis to ‘difficult’ data structures from the recent literature and develop two synthetic data generators ...
Susana Nascimento, Boris G Mirkin
exaly   +2 more sources

Thematic Cluster Analysis of the L2 Experience Interview Corpus

2017
This chapter addresses two research questions. First, what semantic clusters are identifiable in the L2 Experience Interview Corpus (Polat 2013a), and second, what can these clusters tell us about the participants’ L2 learning experience? Starting with our transcribed sentences (called “elementary contexts”) as the basic unit of analysis, the T-Lab ...
Eric Friginal   +3 more
exaly   +2 more sources

Learning semantic and thematic vocabulary clusters through embedded instruction: effects on very young English learners’ vocabulary acquisition and retention

Applied Linguistics Review, 2021
Research has suggested an interference effect for words taught in semantic clusters due to the semantic links connecting the words. Thematic clustering of vocabulary is an alternative method of presenting new words to second language (L2) learners ...
J. McDonald, B. Reynolds
semanticscholar   +1 more source

Clustering Thematic Information in Social Media

Proceedings of the 32nd International Conference on Computer Graphics and Vision, 2022
The constant growth in the number of users of the Internet and the improvement in technical capabilities of communications allow the use of various tools for the rapid notification of the population about the events occurring in the world. Depending on the type of source, models of information dissemination differ.
Mikhail Sergeevich Ulizko   +4 more
openaire   +1 more source

Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis

Journal of Behavioral and Experimental Finance, 2021
Artificial intelligence (AI) and machine learning (ML) are two related technologies that are emergent in financial scholarship. However, no review, to date, has offered a wholistic retrospection of this research.
John W. Goodell   +3 more
semanticscholar   +1 more source

Thematic Clustering

Industry and Higher Education, 2000
The authors first present a macro-level background to the idea of ‘connectivity’, based on a review of the systemic view in the literature on innovation and industrial/business clusters, linking the two to demonstrate the relevance of organizational forms (clusters) to the innovation process. They then identify management considerations for organizing
Jay Mitra, Harry Matlay
openaire   +1 more source

Space-adaptive Artwork Placement Based on Content Similarities for Curating Thematic Spaces in a Virtual Museum

ACM Journal on Computing and Cultural Heritage, 2023
Virtual Reality (VR) provides curators with the tools to design immersive 3D exhibition spaces. However, manually positioning artworks in VR is labor-intensive, and most existing automated methods are limited in considering both artwork content and ...
Hayun Kim   +3 more
semanticscholar   +1 more source

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