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Тр. МИАН, 2009, том 265, страницы 189–210
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Эта публикация цитируется в 6 научных статьях (всего в 6 статьях)
Symmetry in Data Mining and Analysis: A Unifying View Based on Hierarchy
F. Murtaghab
a Science Foundation Ireland, Dublin, Ireland
b Department of Computer Science, University of London, Egham, UK
Аннотация:
Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. The data sets themselves are explicitly linked as a form of representation to an observational, or otherwise empirical, domain of interest. “Structure” has long been understood as symmetry which can take many forms with respect to any transformation, including point, translational, rotational, and many others. Symmetries directly point to invariants that pinpoint intrinsic properties of the data and of the background empirical domain of interest. As our data models change, so too do our perspectives on analyzing data. The structures in data surveyed here are based on hierarchy, represented as $p$-adic numbers or an ultrametric topology.
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Англоязычная версия:
Proceedings of the Steklov Institute of Mathematics, 2009, 265, 177–198
Реферативные базы данных:
    
УДК:
519.72
Поступило в январе 2009 г.
Язык публикации: английский
Образец цитирования:
F. Murtagh, “Symmetry in Data Mining and Analysis: A Unifying View Based on Hierarchy”, Избранные вопросы математической физики и $p$-адического анализа, Сборник статей, Тр. МИАН, 265, МАИК «Наука/Интерпериодика», М., 2009, 189–210; Proc. Steklov Inst. Math., 265 (2009), 177–198
Цитирование в формате AMSBIB
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http://mi.mathnet.ru/tm834 http://mi.mathnet.ru/rus/tm/v265/p189
Citing articles on Google Scholar:
Russian citations,
English citations
Related articles on Google Scholar:
Russian articles,
English articles
Эта публикация цитируется в следующих статьяx:
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Contreras P., Murtagh F., “Fast, Linear Time Hierarchical Clustering Using the Baire Metric”, J. Classif., 29:2 (2012), 118–143
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Murtagh F., Contreras P., “Algorithms for Hierarchical Clustering: an Overview”, Wiley Interdiscip. Rev.-Data Mining Knowl. Discov., 2:1 (2012), 86–97
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Kane J., Naumov P., “Symmetries and Epistemic Reasoning”, Computational Logic in Multi-Agent Systems, Clima XIV, Lecture Notes in Artificial Intelligence, 8143, eds. Leite J., Son T., Torroni P., VanDerTorre L., Woltran S., Springer-Verlag Berlin, 2013, 190–205
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Kane J., Naumov P., “Symmetry in Information Flow”, Ann. Pure Appl. Log., 165:1, SI (2014), 253–265
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Murtagh F., “Big Data Scaling Through Metric Mapping: Exploiting the Remarkable Simplicity of Very High Dimensional Spaces Using Correspondence Analysis”, Data Science: Innovative Developments in Data Analysis and Clustering, Studies in Classification Data Analysis and Knowledge Organization, eds. Palumbo F., Montanari A., Vichi M., Springer International Publishing Ag, 2017, 295–306
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Muntean M., Brandas C., Cirstea T., “Framework For a Symmetric Integration Approach”, Symmetry-Basel, 11:2 (2019), 224
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