Constructing Interactive Visual Classification, Clustering and Dimension Reduction Models for n-D Data

Informatics

Boris Kovalerchuk¹  Dmytro Dovhalets¹ 

[1] Department of Computer Science, Central Washington University, Ellensburg, WA, 98926, USA;

关键词: interactive visualization;    classification;    clustering;    dimension reduction;    multidimensional visual analytics;    machine learning;    knowledge discovery;    linear relations;   

DOI  :  10.3390/informatics4030023

来源: DOAJ

【 摘 要 】

Abstract: The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the loss of information, occlusion, and clutter continue to be a challenge. This paper proposes and explores a new interactive method for visual discovery of n-D relations for supervised learning. The method includes automatic, interactive, and combined algorithms for discovering linear relations, dimension reduction, and generalization for non-linear relations. This method is a special category of reversible General Line Coordinates (GLC). It produces graphs in 2-D that represent n-D points losslessly, i.e., allowing the restoration of n-D data from the graphs. The projections of graphs are used for classification. The method is illustrated by solving machine-learning classification and dimension-reduction tasks from the domains of image processing, computer-aided medical diagnostics, and finance. Experiments conducted on several datasets show that this visual interactive method can compete in accuracy with analytical machine learning algorithms.

【 授权许可】

Unknown