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New Ordering-Based Pruning Metrics for Ensembles of Classifiers in Imbalanced Datasets Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Hierarchical Gaussian Mixture Model with Objects Attached to Terminal and Non-terminal Dendrogram Nodes

Authors : Łukasz P. Olech, Mariusz Paradowski

Published in: Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Publisher: Springer International Publishing

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Abstract

A hierarchical clustering algorithm based on Gaussian mixture model is presented. The key difference to regular hierarchical mixture models is the ability to store objects in both terminal and nonterminal nodes. Upper levels of the hierarchy contain sparsely distributed objects, while lower levels contain densely represented ones. As it was shown by experiments, this ability helps in noise detection (modeling). Furthermore, compared to regular hierarchical mixture model, the presented method generates more compact dendrograms with higher quality measured by adopted F-measure.

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Metadata
Title
Hierarchical Gaussian Mixture Model with Objects Attached to Terminal and Non-terminal Dendrogram Nodes
Authors
Łukasz P. Olech
Mariusz Paradowski
Copyright Year
2016
Book
Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Print ISBN: 978-3-319-26225-3

Electronic ISBN: 978-3-319-26227-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-26227-7_18

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