A comparative study of data-dependent approaches without learning in measuring similarities of data objects

DOI10.1007/s10618-019-00660-0zbMath1458.68198OpenAlexW2988948553WikidataQ126867456 ScholiaQ126867456MaRDI QIDQ2218403

Sunil Aryal, Gholamreza Haffari, Takashi Washio, Kai Ming Ting

Publication date: 15 January 2021

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-019-00660-0

zbMATH Keywords

distance measuresrank transformation\( \ell_p\)-norm\(m_p\)-dissimilaritydata-dependent similarity measuresLin's probabilistic similarity

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Information storage and retrieval of data (68P20) Computational aspects of data analysis and big data (68T09)

Uses Software

• UCI-ml
• Scikit
• word2vec
• CIFAR
• GitHub
• IsolationForest
• ALOI

Cites Work

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• Survey on distance metric learning and dimensionality reduction in data mining
• SimUSF: an efficient and effective similarity measure that is invariant to violations of the interval scale assumption
• Metric Learning: A Survey
• On the Theory of Scales of Measurement
• Rank Transformations as a Bridge Between Parametric and Nonparametric Statistics
• Locality-sensitive hashing scheme based on p-stable distributions
• Encyclopedia of Distances
• Random forests

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