2026-06-02T05:22:49Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/38572021-11-10T09:38:23Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

00925njm 22002777a 4500 dc Corchado, Emilio author Baruque, Bruno author 2012-01 This study presents a novel version of the Visualization Induced Self-Organizing Map based on the application of a new fusion algorithm for summarizing the results of an ensemble of topology-preserving mapping models. The algorithm is referred to as Weighted Voting Superposition (WeVoS). Its main feature is the preservation of the topology of the map, in order to obtain the most accurate possible visualization of the data sets under study. To do so, a weighted voting process between the units of the maps in the ensemble takes place, in order to determine the characteristics of the units of the resulting map. Several different quality measures are applied to this novel neural architecture known as WeVoS-ViSOM and the results are analyzed, so as to present a thorough study of its capabilities. To complete the study, it has also been compared with the well-know SOM and its fusion version, with the WeVoS-SOM and with two other previously devised fusion Fusion by Euclidean Distance and Fusion by Voronoi Polygon Similarity—based on the analysis of the same quality measures in order to present a complete analysis of its capabilities. All three summarization methods were applied to three widely used data sets from the UCI Repository. A rigorous performance analysis clearly demonstrates that the novel fusion algorithm outperforms the other single and summarization methods in terms of data sets visualization 0925-2312 http://hdl.handle.net/10259/3857 10.1016/j.neucom.2011.01.027 Topology-preserving maps Unsupervised learning Data visualization Ensembles Summarization algorithm WeVoS-ViSOM: an ensemble summarization algorithm for enhanced data visualization