Title :
Gaussian Mixture Model Cluster Forest
Author :
Jan Janouek;Petr Gajdo; Radeck?;V?clav Sn?el
Author_Institution :
FEECS, Dept. of Comput. Sci., VSB Tech. Univ. of Ostrava, Ostrava, Czech Republic
Abstract :
Random Forest (RF) classification algorithm is widely used in the area of information retrieval and became a basis for some extended branches of classification and/or regression algorithms. Cluster Forest (CF) represents a particular branch, and brings usually better results than individual clustering algorithms. This article describes a new ensemble clustering algorithm based on CF that internally uses a probabilistic model called Gaussian Mixture Model (GMM). Finally, Expectation-maximization algorithm is used for estimation of GMM parameters. The proposed ensemble clustering algorithm will be compared with several different approaches and tested on eight datasets.
Keywords :
"Clustering algorithms","Measurement","Gaussian mixture model","Radio frequency","Partitioning algorithms","Clustering methods"
Conference_Titel :
Machine Learning and Applications (ICMLA), 2015 IEEE 14th International Conference on
DOI :
10.1109/ICMLA.2015.12