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Klusterointialgoritmien vertailu

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Authors
Nättilä, Severi
Date
2021
Discipline
TietotekniikkaMathematical Information Technology
Copyright
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.

 
Tutkielmassa tutustutaan ryhmittelyn perusteisiin, todennäköisyysmallipohjaisen sekä ei-parametrisen datan klusterointiin ja menetelmiin. Klusterointimenetelmistä käydään läpi: EM-algoritmi, k-means, k-medoids, k-modes ja k-prototypes. Tutkitaan millaisendatan käsittelyyn menetelmät soveltuvat ja miksi juuri niitä hyödynnetään ryhmittelyssä.
 
The point of this study is to focus on the basics of clustering, probability model-based approaches and non-parametric approaches. The clustering methods that this study focuses on are: EM-algorithm, k-means, k-medoids, k-modes and k-prototypes. This study also focuses on how these methods are applied in clustering of data and why they are used.
 
Keywords
klusterointi GMM EM-algoritmi k-means k-medoids k-modes k-prototypes ohjaamaton oppiminen numeerinen data kategorinen data data algoritmit matemaattiset menetelmät tiedonlouhinta hyödyntäminen
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http://urn.fi/URN:NBN:fi:jyu-202106103622

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