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Evandro S. Ortigossa, Fábio Felix Dias and Diego Carvalho do Nascimento
The exploration and analysis of multidimensional data can be pretty complex tasks, requiring sophisticated tools able to transform large amounts of data bearing multiple parameters into helpful information. Multidimensional projection techniques figure a...
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Marian B. Gorzalczany and Filip Rudzinski
In this paper, we briefly present several modifications and generalizations of the concept of self-organizing neural networks?usually referred to as self-organizing maps (SOMs)?to illustrate their advantages in applications that range from high-dimension...
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Sheikh S. Abdullah, Neda Rostamzadeh, Kamran Sedig, Amit X. Garg and Eric McArthur
Recent advancement in EHR-based (Electronic Health Record) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional data available in EHRs creates great opportunities for machine learning techniques such...
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Bardia Rafieian, Pedro Hermosilla and Pere-Pau Vázquez
In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim ...
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Karaj Khosla, Indra Prakash Jha, Ajit Kumar and Vibhor Kumar
Dimension reduction is often used for several procedures of analysis of high dimensional biomedical data-sets such as classification or outlier detection. To improve the performance of such data-mining steps, preserving both distance information and loca...
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Anderson Gregório Marques Soares, Elvis Thermo Carvalho Miranda, Rodrigo Santos do Amor Divino Lima, Carlos Gustavo Resque dos Santos and Bianchi Serique Meiguins
The Treemap is one of the most relevant information visualization (InfoVis) techniques to support the analysis of large hierarchical data structures or data clusters. Despite that, Treemap still presents some challenges for data representation, such as t...
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Kyriakos Apostolidis, Christos Kokkotis, Evangelos Karakasis, Evangeli Karampina, Serafeim Moustakidis, Dimitrios Menychtas, Georgios Giarmatzis, Dimitrios Tsiptsios, Konstantinos Vadikolias and Nikolaos Aggelousis
Stroke remains a predominant cause of mortality and disability worldwide. The endeavor to diagnose stroke through biomechanical time-series data coupled with Artificial Intelligence (AI) poses a formidable challenge, especially amidst constrained partici...
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Rui Xin, Tinghua Ai, Ruoxin Zhu, Bo Ai, Min Yang and Liqiu Meng
Metaphor are commonly used rhetorical devices in linguistics. Among the various types, spatial metaphors are relatively common because of their intuitive and sensible nature. There are also many studies that use spatial metaphors to express non-location ...
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Frank Schoeneman, Varun Chandola, Nils Napp, Olga Wodo and Jaroslaw Zola
Scientific data, generated by computational models or from experiments, are typically results of nonlinear interactions among several latent processes. Such datasets are typically high-dimensional and exhibit strong temporal correlations. Better understa...
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Lucas de Carvalho Pagliosa and Alexandru C. Telea
RadViz is one of the few methods in Visual Analytics able to project high-dimensional data and explain formed structures in terms of data variables. However, RadViz methods have several limitations in terms of scalability in the number of variables, ambi...
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Nan Xu, Zhiming Zhang and Yongming Liu
Structural Health Monitoring requires the continuous assessment of a structure?s operational conditions, which involves the collection and analysis of a large amount of data in both spatial and temporal domains. Conventionally, both data-driven and physi...
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Tulsi Patel, Mark W. Jones and Thomas Redfern
We present a novel approach to providing greater insight into the characteristics of an unlabelled dataset, increasing the efficiency with which labelled datasets can be created. We leverage dimension-reduction techniques in combination with autoencoders...
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Yu Wang, Alister Machado and Alexandru Telea
Visualization techniques for understanding and explaining machine learning models have gained significant attention. One such technique is the decision map, which creates a 2D depiction of the decision behavior of classifiers trained on high-dimensional ...
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Weili Zeng, Zhengfeng Xu, Zhipeng Cai, Xiao Chu and Xiaobo Lu
The aircraft trajectory clustering analysis in the terminal airspace is conducive to determining the representative route structure of the arrival and departure trajectory and extracting their typical patterns, which is important for air traffic manageme...
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