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Consensus Clustering: Self- Organizing Map, K- Means Algorithm, Metric, Data Analysis, Machine Learning -

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englanti
2026-03-19
127,22 € 195,73 €

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High Quality Content by WIKIPEDIA articles! High Quality Content by WIKIPEDIA articles! Clustering is the assignment of objects into groups (called clusters) so that objects from the same cluster are more similar to each other than objects from different clusters. Often similarity is assessed according to a distance measure. Clustering is a common technique for statistical data analysis, which is used in ma ... Täydellinen kuvaus

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Kuvaus

High Quality Content by WIKIPEDIA articles! High Quality Content by WIKIPEDIA articles! Clustering is the assignment of objects into groups (called clusters) so that objects from the same cluster are more similar to each other than objects from different clusters. Often similarity is assessed according to a distance measure. Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Consensus clustering has emerged as an important elaboration of the classical clustering problem. Consensus clustering, also called aggregation of clustering (or partitions), refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better fit in some sense than the existing clusterings. Consensus clustering is thus the problem of reconciling clustering information about the same data set coming from different sources or from different runs of the same algorithm.

Lisätietoja

Julkaisija OmniScriptum
Julkaisuvuosi 2026
Kannen tyyppi Pehmeäkantinen
EAN 9786131174483
Kirjoita oma arvostelusi
Arvostelet: Consensus Clustering: Self- Organizing Map, K- Means Algorithm, Metric, Data Analysis, Machine Learning
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127,22 € 195,73 €