Mapping Architecture. Each point are calculated according to Equation 1 from Kadogan [12]. 

Mapping Architecture. Each point are calculated according to Equation 1 from Kadogan [12]. 

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Abstract The twenty first century sees the tremendous advancement of computer and machine technologies that are able to produce ginormous amout of data. Current software architecture, management and analysis approaches are unable to cope with the flood of data. The challenge of understanding large and complex data includes issues such as clutter, p...

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... 2 involves mapping each information object onto the star coordinate axes. The axes C 1 ,...,C 6 denoted the fields or dimensions share a common origin, which in the Cartesian Coordinate system may be conveniently denoted by (0,0) shown in Figure 3. Each field vector f 1, ...,f 6 is calculated by multiplying the distance with its corresponding unit vector, oriented in a direction along the axis,C j . ...

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... SCs have been used in literature for decision tree construction to classify different objects [53], perform cluster analysis [54], finding trends for decision making [55], and visualizing linearly separable clusters [56]. However, for our data sets with hundreds of dimensions, it would not be possible to interact with the vectors in an intuitive way. ...
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Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of thousands of mass-to-charge ratios (m/z-ratios), each linked to an individual molecular ion species. Traditional analysis tools focus on few individual m/z-ratios, which neglects most of the data. Recently, clustering methods of the spectral information have emerged, but faithful detection of all relevant image regions is not always possible. We propose an interactive visual analysis approach that considers all available information in coordinated views of image and spectral space visualizations, where the spectral space is treated as a multi-dimensional space. We use non-linear embeddings of the spectral information to interactively define clusters and respective image regions. Of particular interest is, then, which of the molecular ion species cause the formation of the clusters. We propose to use linear embeddings of the clustered data, as they allow for relating the projected views to the given dimensions. We document the effectiveness of our approach in analyzing matrix-assisted laser desorption/ionization (MALDI-2) imaging data with ground truth obtained from histological images.
... Human Computer Interaction (HCI) principles are highly relevant in this domain and the user experience could be enhanced by incorporating principles such as gestault laws of grouping, and providing frequent and appropriate feedback (Seokyeon et al. 2015). Similarly, the use of colours is a simple way to highlight similarities or differences in the data (Elaiza et al. 2014). As mentioned in Sect. ...
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The continual growth of big data necessitates efficient ways of analysing these large datasets. Data visualisation and visual analytics has been identified as a key tool in big data analysis because they draw on the human visual and cognitive capabilities to analyse data quickly, intuitively and interactively. However, current visualisation tools and visual analytical systems fall short of providing a seamless user experience and several improvements could be made to current commercially available visualisation tools. By conducting a systematic literature review, requirements of visualisation tools were identified and categorised into six groups: dimensionality reduction, data reduction, scalability and readability, interactivity, fast retrieval of results, and user assistance. The most common themes found in the literature were dimensionality reduction and interactive data exploration.
... Clustering methods have been highlighted in many research and applied in many domains [9][10][11][12][13]. In clustering the idea is not to predict the target class as like classification, it is more ever trying to group the similar kind of things by considering the most satisfied conditions all the items in the same group should be similar and no two different group items should not be similar [14]. ...
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Students' performance is a key point to get a better first impression during a job interview with an employer. However, there are several factors, which affect students' performances during their study. One of them is their learning style, which is under Neurolinguistic Programming (NLP) approach. Learning style is divided into a few behavioral categories, Visual, Auditory and Kinesthetics (VAK). This paper addresses the evaluation of clustering methods for the identification of learning style based on system preferences. It starts with the distribution of questionnaires to acquire the information on the VAK for each student. About 167 respondents in the Faculty of Computer and Mathematical Science are collected. It is then pre- processed to prepare the data for clustering method evaluations. Three clustering methods; Simple K-Mean, Hierarchical and Density-Based Spatial Clustering of Applications with Noise are evaluated. The findings show that Simple K-Mean offers the most accurate prediction. Upon completion, by using the dataset, Simple K-Means technique estimated four clusters that yield the highest accuracy of 74.85 % compared to Hierarchical Clustering, which estimated four clusters and Density- Based Spatial Clustering of Applications with Noise which estimated three clusters with 52.69% and 61.68 % respectively. The clustering method demonstrates the capability of categorizing the learning style of students based on three categories; visual, auditory and kinesthetic. This outcome would be beneficial to lecturers or teachers in university and school with an automatically clustering the students' learning style and would assist them in teaching and learning, respectively.
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Solar energy supplies pure environmental-friendly and limitless energy resource for human. Although the cost of solar panels has declined rapidly, technology gaps still exist for achieving cost-effective scalable deployment combined with storage technologies to provide reliable, dispatchable energy. However, it is difficult to analyze a solar data, in which data was added in every 10 min by the sensors in a short time. These data can be analyzed easier and faster with the help of data visualization. One of the popular data visualization methods for displaying massive quantity of data is parallel coordinates plot (PCP). The problem when using this method is this abundance of data can cause the polylines to overlap on each other and clutter the visualization. Thus, it is difficult to comprehend the relationship that exists between the parameters of solar data such as power rate produced by solar panel, duration of daylight in a day, and surrounding temperature. Furthermore, the density of overlapped data also cannot be determined. The solution is to implement clutter-reduction technique to parallel coordinate plot. Even though there are various clutter-reduction techniques available for visualization, they are not suitable for every situation of visualization. Thus this research studies a wide range of clutter-reduction techniques that has been implemented in visualization, identifies the common features available in clutter-reduction technique, produces a conceptual framework of clutter-reduction technique as well as proposes the suitable features to be added in parallel coordinates plot of solar energy data to reduce visual clutter.