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K-Means Clustering for Problems with Periodic Attributes
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SYSNO ASEP 0328432 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název K-Means Clustering for Problems with Periodic Attributes Překlad názvu Shlukovací algoritmus K-Means v problémech s periodickými atributy Tvůrce(i) Vejmelka, Martin (UIVT-O) SAI, RID, ORCID
Musílek, P. (CA)
Paluš, Milan (UIVT-O) RID, SAI, ORCID
Pelikán, Emil (UIVT-O) SAI, RIDZdroj.dok. International Journal of Pattern Recognition and Artificial Intelligence - ISSN 0218-0014
Roč. 23, č. 4 (2009), s. 721-743Poč.str. 23 s. Jazyk dok. eng - angličtina Země vyd. SG - Singapur Klíč. slova clustering algorithms ; similarity measures ; K-means ; periodic attributes Vědní obor RIV BB - Aplikovaná statistika, operační výzkum CEP 1ET400300513 GA AV ČR - Akademie věd CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000267117500003 EID SCOPUS 67650703129 DOI 10.1142/S0218001409007338 Anotace The K-means algorithm is very popular in the machine learning community due to its inherent simplicity. However, in its basic form, it is not suitable for use in problems which contain periodic attributes, such as oscillator phase, hour of day or directional heading. A commonly used technique of trigonometrically encoding periodic input attributes to artificially generate the required topology introduces a systematic error. In this paper, a metric which induces a conceptually correct topology for periodic attributes is embedded into the K-means algorithm. This requires solving a non-convex minimization problem in the maximization step. Results of numerical experiments comparing the proposed algorithm to K-means with trigonometric encoding on synthetically generated data are reported. The advantage of using the proposed K-means algorithm is also shown on a real example using gas load data to build simple predictive models. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2010
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